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: "natural language does not emerge naturally in multi agent dialog"
  Abstract: A number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the protocols developed by the agents, all learned without any human supervision! #R##N#In this paper, using a Task and Tell reference game between two agents as a testbed, we present a sequence of 'negative' results culminating in a 'positive' one -- showing that while most agent-invented languages are effective (i.e. achieve near-perfect task rewards), they are decidedly not interpretable or compositional. #R##N#In essence, we find that natural language does not emerge 'naturally', despite the semblance of ease of natural-language-emergence that one may gather from recent literature. We discuss how it is possible to coax the invented languages to become more and more human-like and compositional by increasing restrictions on how two agents may communicate.

Connected through hyperedge HE-1 (induced by source paper "on the interaction between supervision and self play in emergent communication"):
  with members:
    - "anti efficient encoding in emergent communication"
    - "emergent linguistic phenomena in multi agent communication games"
    - "capacity bandwidth and compositionality in emergent language learning"
    - "ease of teaching and language structure from emergent communication"
    - "on the pitfalls of measuring emergent communication"
    - "mastering emergent language learning to guide in simulated navigation"
    - "countering language drift via visual grounding"
    - "emergence of compositional language with deep generational transmission"

Connected through hyperedge HE-2 (induced by source paper "emergent communication with world models"):
  with members:
    - "biases for emergent communication in multi agent reinforcement learning"
    - "on the pitfalls of measuring emergent communication"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "compositional obverter communication learning from raw visual input"

Connected through hyperedge HE-3 (induced by source paper "incentivizing the emergence of grounded discrete communication between general agents"):
  with members:
    - "on the pitfalls of measuring emergent communication"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "compositional obverter communication learning from raw visual input"
    - "scalable agent alignment via reward modeling a research direction"
    - "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"
    - "emergence of grounded compositional language in multi agent populations"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-4 (induced by source paper "on emergent communication in competitive multi agent teams"):
  with members:
    - "measuring compositionality in representation learning"
    - "on the pitfalls of measuring emergent communication"
    - "strong and simple baselines for multimodal utterance embeddings"
    - "emergence of compositional language with deep generational transmission"
    - "multimodal language analysis with recurrent multistage fusion"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "emergence of communication in an interactive world with consistent speakers"

Connected through hyperedge HE-5 (induced by source paper "exploring structural inductive biases in emergent communication"):
  with members:
    - "on the interaction between supervision and self play in emergent communication"
    - "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"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-6 (induced by source paper "compositional languages emerge in a neural iterated learning model"):
  with members:
    - "measuring compositionality in representation learning"
    - "ease of teaching and language structure from emergent communication"
    - "emergence of compositional language with deep generational transmission"
    - "compositional obverter communication learning from raw visual input"
    - "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"
    - "emergence of grounded compositional language in multi agent populations"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "visual hide and seek"):
  with members:
    - "two body problem collaborative visual task completion"
    - "emergent tool use from multi agent autocurricula"
    - "emergent coordination through competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-8 (induced by source paper "align2ground weakly supervised phrase grounding guided by image caption alignment"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "multi level multimodal common semantic space for image phrase grounding"
    - "using syntax to ground referring expressions in natural images"
    - "stacked cross attention for image text matching"
    - "deep sets"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "understanding visual ads by aligning symbols and objects using co attention"
    - "learning two branch neural networks for image text matching tasks"

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)