human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "symbol emergence in robotics a survey"
  Abstract: Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.

Connected through hyperedge HE-1 (induced by source paper "spatial concept based navigation with human speech instructions via probabilistic inference on bayesian generative model"):
  with members:
    - "steap simultaneous trajectory estimation and planning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "visual reinforcement learning with imagined goals"
    - "improved and scalable online learning of spatial concepts and language models with mapping"
    - "online spatial concept and lexical acquisition with simultaneous localization and mapping"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "spatial concept acquisition for a mobile robot that integrates self localization and unsupervised word discovery from spoken sentences"

Connected through hyperedge HE-2 (induced by source paper "a survey of knowledge representation in service robotics"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "the limits and potentials of deep learning for robotics"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "blitznet a real time deep network for scene understanding"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "affordancenet an end to end deep learning approach for object affordance detection"

Connected through hyperedge HE-3 (induced by source paper "semantic speech retrieval with a visually grounded model of untranscribed speech"):
  with members:
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "visual features for context aware speech recognition"
    - "sequence to sequence models can directly translate foreign speech"
    - "learning word like units from joint audio visual analysis"
    - "learning modality invariant representations for speech and images"

Connected through hyperedge HE-4 (induced by source paper "symbol emergence as an interpersonal multimodal categorization"):
  with members:
    - "emergent linguistic phenomena in multi agent communication games"
    - "online spatial concept and lexical acquisition with simultaneous localization and mapping"
    - "serket an architecture for connecting stochastic models to realize a large scale cognitive model"
    - "emergent translation in multi agent communication"
    - "spatial concept acquisition for a mobile robot that integrates self localization and unsupervised word discovery from spoken sentences"
    - "sensorimotor input as a language generalisation tool a neurorobotics model for generation and generalisation of noun verb combinations with sensorimotor inputs"
    - "multi agent cooperation and the emergence of natural language"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "neuro serket development of integrative cognitive system through the composition of deep probabilistic generative models"):
  with members:
    - "cross modal variational auto encoder with distributed latent spaces and associators"
    - "multimodal generative models for scalable weakly supervised learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "online spatial concept and lexical acquisition with simultaneous localization and mapping"
    - "serket an architecture for connecting stochastic models to realize a large scale cognitive model"
    - "spatial concept acquisition for a mobile robot that integrates self localization and unsupervised word discovery from spoken sentences"
    - "nonparametric bayesian double articulation analyzer for direct language acquisition from continuous speech signals"
    - "variational deep embedding an unsupervised and generative approach to clustering"

Connected through hyperedge HE-6 (induced by source paper "multimodal one shot learning of speech and images"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "zero shot keyword spotting for visual speech recognition in the wild"
    - "in defense of the triplet loss for person re identification"
    - "one shot visual imitation learning via meta learning"
    - "prototypical networks for few shot learning"
    - "a simple neural attentive meta learner"
    - "attentive recurrent comparators"
    - "matching networks for one shot learning"

Connected through hyperedge HE-7 (induced by source paper "symbol emergence in cognitive developmental systems a survey"):
  with members:
    - "interactive natural language acquisition in a multi modal recurrent neural architecture"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "online spatial concept and lexical acquisition with simultaneous localization and mapping"
    - "serket an architecture for connecting stochastic models to realize a large scale cognitive model"
    - "detection of social signals for recognizing engagement in human robot interaction"
    - "grounded language learning in a simulated 3d world"
    - "grounding spatio semantic referring expressions for human robot interaction"
    - "discovering objects and their relations from entangled scene representations"

Connected through hyperedge HE-8 (induced by source paper "developmental bayesian optimization of black box with visual similarity based transfer learning"):
  with members:
    - "learning flexible and reusable locomotion primitives for a microrobot"
    - "comparing task simplifications to learn closed loop object picking using deep reinforcement learning"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "unscented bayesian optimization for safe robot grasping"
    - "robots that can adapt like animals"
    - "3d shapenets a deep representation for volumetric shapes"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.AI(Artificial Intelligence)