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: "emergence of communication in an interactive world with consistent speakers"
  Abstract: Training agents to communicate with one another given task-based supervision only has attracted considerable attention recently, due to the growing interest in developing models for human-agent interaction. Prior work on the topic focused on simple environments, where training using policy gradient was feasible despite the non-stationarity of the agents during training. In this paper, we present a more challenging environment for testing the emergence of communication from raw pixels, where training using policy gradient fails. We propose a new model and training algorithm, that utilizes the structure of a learned representation space to produce more consistent speakers at the initial phases of training, which stabilizes learning. We empirically show that our algorithm substantially improves performance compared to policy gradient. We also propose a new alignment-based metric for measuring context-independence in emerged communication and find our method increases context-independence compared to policy gradient and other competitive baselines.

Connected through hyperedge HE-1 (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-2 (induced by source paper "emergence of pragmatics from referential game between theory of mind agents"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "compositional obverter communication learning from raw visual input"
    - "planning inference and pragmatics in sequential language games"
    - "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"
    - "learning with opponent learning awareness"

Connected through hyperedge HE-3 (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"
    - "how agents see things on visual representations in an emergent language game"

Connected through hyperedge HE-4 (induced by source paper "on memory mechanism in multi agent reinforcement learning"):
  with members:
    - "r maddpg for partially observable environments and limited communication"
    - "on the pitfalls of measuring emergent communication"
    - "bayes tomop a fast detection and best response algorithm towards sophisticated opponents"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "compositional obverter communication learning from raw visual input"
    - "machine theory of mind"
    - "emergent communication in a multi modal multi step referential game"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-5 (induced by source paper "on the pitfalls of measuring emergent communication"):
  with members:
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "compositional obverter communication learning from raw visual input"
    - "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"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"

Connected through hyperedge HE-6 (induced by source paper "measuring compositionality in representation learning"):
  with members:
    - "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"
    - "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"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-7 (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"
    - "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"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"

Connected through hyperedge HE-8 (induced by source paper "developmentally motivated emergence of compositional communication via template transfer"):
  with members:
    - "ease of teaching and language structure from emergent communication"
    - "on the pitfalls of measuring emergent communication"
    - "egg a toolkit for research on emergence of language in games"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "compositional obverter communication learning from raw visual input"
    - "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"

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)