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: "on the interaction between supervision and self play in emergent communication"
  Abstract: A promising approach for teaching artificial agents to use natural language involves using human-in-the-loop training. However, recent work suggests that current machine learning methods are too data inefficient to be trained in this way from scratch. In this paper, we investigate the relationship between two categories of learning signals with the ultimate goal of improving sample efficiency: imitating human language data via supervised learning, and maximizing reward in a simulated multi-agent environment via self-play (as done in emergent communication), and introduce the term supervised self-play (S2P) for algorithms using both of these signals. We find that first training agents via supervised learning on human data followed by self-play outperforms the converse, suggesting that it is not beneficial to emerge languages from scratch. We then empirically investigate various S2P schedules that begin with supervised learning in two environments: a Lewis signaling game with symbolic inputs, and an image-based referential game with natural language descriptions. Lastly, we introduce population based approaches to S2P, which further improves the performance over single-agent methods.

Connected through hyperedge HE-1 (induced by source paper "exploring structural inductive biases in emergent communication"):
  with members:
    - "measuring compositionality in representation learning"
    - "ease of teaching and language structure from emergent communication"
    - "clutrr a diagnostic benchmark for inductive reasoning from text"
    - "egg a toolkit for research on emergence of language in games"
    - "how agents see things on visual representations in an emergent language game"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-2 (induced by source paper "capacity bandwidth and compositionality in emergent language learning"):
  with members:
    - "linguistic generalization and compositionality in modern artificial neural networks"
    - "anti efficient encoding in emergent communication"
    - "the compositionality of neural networks integrating symbolism and connectionism"
    - "word order biases in deep agent emergent communication"
    - "measuring compositionality in representation learning"
    - "countering language drift via visual grounding"
    - "compositional generalization through meta sequence to sequence learning"
    - "pytorch an imperative style high performance deep learning library"

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)