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: "interactive natural language acquisition in a multi modal recurrent neural architecture"
  Abstract: ABSTRACTFor the complex human brain that enables us to communicate in natural language, we gathered good understandings of principles underlying language acquisition and processing, knowledge about sociocultural conditions, and insights into activity patterns in the brain. However, we were not yet able to understand the behavioural and mechanistic characteristics for natural language and how mechanisms in the brain allow to acquire and process language. In bridging the insights from behavioural psychology and neuroscience, the goal of this paper is to contribute a computational understanding of appropriate characteristics that favour language acquisition. Accordingly, we provide concepts and refinements in cognitive modelling regarding principles and mechanisms in the brain and propose a neurocognitively plausible model for embodied language acquisition from real-world interaction of a humanoid robot with its environment. In particular, the architecture consists of a continuous time recurrent neural netwo...

Connected through hyperedge HE-1 (induced by source paper "multimodal grounding for language processing"):
  with members:
    - "quantifying the visual concreteness of words and topics in multimodal datasets"
    - "multimodal named entity recognition for short social media posts"
    - "modulating early visual processing by language"
    - "multimodal machine learning a survey and taxonomy"
    - "gated attention architectures for task oriented language grounding"
    - "emergence of grounded compositional language in multi agent populations"
    - "teaching machines to describe images via natural language feedback"
    - "recurrent models for situation recognition"

Connected through hyperedge HE-2 (induced by source paper "symbol emergence in cognitive developmental systems a survey"):
  with members:
    - "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"
    - "spatial concept acquisition for a mobile robot that integrates self localization and unsupervised word discovery from spoken sentences"

Connected through hyperedge HE-3 (induced by source paper "sensorimotor input as a language generalisation tool a neurorobotics model for generation and generalisation of noun verb combinations with sensorimotor inputs"):
  with members:
    - "reasoning about pragmatics with neural listeners and speakers"
    - "a cognitive neural architecture able to learn and communicate through natural language"
    - "controlling recurrent neural networks by conceptors"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"
    - "grounding the lexical semantics of verbs in visual perception using force dynamics and event logic"

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.CL(Computation and Language)