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: "grounding spatio semantic referring expressions for human robot interaction"
  Abstract: The human language is one of the most natural interfaces for humans to interact with robots. This paper presents a robot system that retrieves everyday objects with unconstrained natural language descriptions. A core issue for the system is semantic and spatial grounding, which is to infer objects and their spatial relationships from images and natural language expressions. We introduce a two-stage neural-network grounding pipeline that maps natural language referring expressions directly to objects in the images. The first stage uses visual descriptions in the referring expressions to generate a candidate set of relevant objects. The second stage examines all pairwise relationships between the candidates and predicts the most likely referred object according to the spatial descriptions in the referring expressions. A key feature of our system is that by leveraging a large dataset of images labeled with text descriptions, it allows unrestricted object types and natural language referring expressions. Preliminary results indicate that our system outperforms a near state-of-the-art object comprehension system on standard benchmark datasets. We also present a robot system that follows voice commands to pick and place previously unseen objects.

Connected through hyperedge HE-1 (induced by source paper "learning to generate unambiguous spatial referring expressions for real world environments"):
  with members:
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "using syntax to ground referring expressions in natural images"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "identifying spatial relations in images using convolutional neural networks"
    - "a joint speaker listener reinforcer model for referring expressions"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "generation and comprehension of unambiguous object descriptions"
    - "a pooling approach to modelling spatial relations for image retrieval and annotation"

Connected through hyperedge HE-2 (induced by source paper "referring relationships"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "neural message passing for quantum chemistry"
    - "convolutional 2d knowledge graph embeddings"
    - "visual translation embedding network for visual relation detection"
    - "modeling relationships in referential expressions with compositional modular networks"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "interactively picking real world objects with unconstrained spoken language instructions"):
  with members:
    - "chainercv a library for deep learning in computer vision"
    - "a joint speaker listener reinforcer model for referring expressions"
    - "ssd single shot multibox detector"
    - "modeling context between objects for referring expression understanding"
    - "visual relationship detection with language priors"
    - "modeling context in referring expressions"
    - "generation and comprehension of unambiguous object descriptions"
    - "vqa visual question answering"

Connected through hyperedge HE-5 (induced by source paper "optimization beyond the convolution generalizing spatial relations with end to end metric learning"):
  with members:
    - "visuospatial skill learning for robots"
    - "metric learning for generalizing spatial relations to new objects"
    - "a differentiable physics engine for deep learning in robotics"
    - "a neural algorithm of artistic style"
    - "learning fine grained image similarity with deep ranking"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "intriguing properties of neural networks"
    - "learning to place new objects in a scene"

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.RO(Robotics)