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: "analogs of linguistic structure in deep representations"
  Abstract: We investigate the compositional structure of message vectors computed by a deep network trained on a communication game. By comparing truth-conditional representations of encoder-produced message vectors to human-produced referring expressions, we are able to identify aligned (vector, utterance) pairs with the same meaning. We then search for structured relationships among these aligned pairs to discover simple vector space transformations corresponding to negation, conjunction, and disjunction. Our results suggest that neural representations are capable of spontaneously developing a "syntax" with functional analogues to qualitative properties of natural language.

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"
    - "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"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-2 (induced by source paper "ease of teaching and language structure from emergent communication"):
  with members:
    - "on the pitfalls of measuring emergent communication"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "emergence of communication in an interactive world with consistent speakers"
    - "learning policy representations in multiagent systems"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication through negotiation"
    - "emergent communication in a multi modal multi step referential game"

Connected through hyperedge HE-3 (induced by source paper "pre learning environment representations for data efficient neural instruction following"):
  with members:
    - "model based reinforcement learning for atari"
    - "the fast and the flexible training neural networks to learn to follow instructions from small data"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "learning language games through interaction"

Connected through hyperedge HE-4 (induced by source paper "on the evaluation of semantic phenomena in neural machine translation using natural language inference"):
  with members:
    - "hypothesis only baselines in natural language inference"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "attention is all you need"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "evaluating discourse phenomena in neural machine translation"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-5 (induced by source paper "emergence of linguistic communication from referential games with symbolic and pixel input"):
  with members:
    - "emergent communication in a multi modal multi step referential game"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergent complexity via multi agent competition"
    - "emergence of grounded compositional language in multi agent populations"
    - "grounded language learning in a simulated 3d world"
    - "asynchronous methods for deep reinforcement learning"
    - "multi agent cooperation and the emergence of natural language"

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)