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: "towards multi agent communication based language learning"
  Abstract: We propose an interactive multimodal framework for language learning. Instead of being passively exposed to large amounts of natural text, our learners (implemented as feed-forward neural networks) engage in cooperative referential games starting from a tabula rasa setup, and thus develop their own language from the need to communicate in order to succeed at the game. Preliminary experiments provide promising results, but also suggest that it is important to ensure that agents trained in this way do not develop an adhoc communication code only effective for the game they are playing

Connected through hyperedge HE-1 (induced by source paper "measuring compositionality in representation learning"):
  with members:
    - "compositional obverter communication learning from raw visual input"
    - "emergence of communication in an interactive world with consistent speakers"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "analogs of linguistic structure in deep representations"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"
    - "visual reference resolution using attention memory for visual dialog"

Connected through hyperedge HE-2 (induced by source paper "deep communicating agents for abstractive summarization"):
  with members:
    - "reinforced video captioning with entailment rewards"
    - "counterfactual multi agent policy gradients"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "efficient summarization with read again and copy mechanism"
    - "starcraft ii a new challenge for reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "controllable abstractive summarization"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "translating neuralese"):
  with members:
    - "context aware captions from context agnostic supervision"
    - "inferring logical forms from denotations"
    - "learning to generate compositional color descriptions"
    - "a joint speaker listener reinforcer model for referring expressions"
    - "visual analysis of hidden state dynamics in recurrent neural networks"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "reasoning about pragmatics with neural listeners and speakers"

Connected through hyperedge HE-4 (induced by source paper "zero resource neural machine translation with multi agent communication game"):
  with members:
    - "cross linguistic differences and similarities in image descriptions"
    - "a teacher student framework for zero resource neural machine translation"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "attention is all you need"
    - "image pivoting for learning multilingual multimodal representations"
    - "convolutional sequence to sequence learning"
    - "zero resource translation with multi lingual neural machine translation"
    - "multimodal pivots for image caption translation"

Connected through hyperedge HE-5 (induced by source paper "emergence of communication in an interactive world with consistent speakers"):
  with members:
    - "compositional obverter communication learning from raw visual input"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication through negotiation"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "mapping instructions and visual observations to actions with reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "a deep reinforcement learning chatbot"):
  with members:
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "parlai a dialog research software platform"
    - "emergence of grounded compositional language in multi agent populations"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "maca a modular architecture for conversational agents"
    - "dialport connecting the spoken dialog research community to real user data"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-7 (induced by source paper "analogs of linguistic structure in deep representations"):
  with members:
    - "translating neuralese"
    - "emergence of grounded compositional language in multi agent populations"
    - "a joint speaker listener reinforcer model for referring expressions"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "question answering with subgraph embeddings"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "a paradigm for situated and goal driven language learning"):
  with members:
    - "learning language games through interaction"
    - "dialog based language learning"
    - "reasoning about pragmatics with neural listeners and speakers"
    - "building machines that learn and think like people"
    - "learning multiagent communication with backpropagation"
    - "learning to communicate to solve riddles with deep distributed recurrent q networks"
    - "a roadmap towards machine intelligence"

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)