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: "emergent communication through negotiation"
  Abstract: Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems. In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction. We introduce two communication protocols -- one grounded in the semantics of the game, and one which is \textit{a priori} ungrounded and is a form of cheap talk. We show that self-interested agents can use the pre-grounded communication channel to negotiate fairly, but are unable to effectively use the ungrounded channel. However, prosocial agents do learn to use cheap talk to find an optimal negotiating strategy, suggesting that cooperation is necessary for language to emerge. We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation.

Connected through hyperedge HE-1 (induced by source paper "the hanabi challenge a new frontier for ai research"):
  with members:
    - "re determinizing information set monte carlo tree search in hanabi"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "evolving agents for the hanabi 2018 cig competition"

Connected through hyperedge HE-2 (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-3 (induced by source paper "cluster based social reinforcement learning"):
  with members:
    - "message dropout an efficient training method for multi agent deep reinforcement learning"
    - "emergent coordination through competition"
    - "personalization of health interventions using cluster based reinforcement learning"
    - "negative update intervals in deep multi agent reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "m 3 rl mind aware multi agent management reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "learning to resolve alliance dilemmas in many player zero sum games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "options as responses grounding behavioural hierarchies in multi agent rl"
    - "learning reciprocity in complex sequential social dilemmas"
    - "emergent coordination through competition"
    - "no press diplomacy modeling multi agent gameplay"
    - "open ended learning in symmetric zero sum games"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "dynamic knowledge routing network for target guided open domain conversation"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "end to end knowledge routed relational dialogue system for automatic diagnosis"
    - "target guided open domain conversation"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "towards deep conversational recommendations"
    - "conversational ai the science behind the alexa prize"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-6 (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 communication in an interactive world with consistent speakers"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication in a multi modal multi step referential game"
    - "learning with opponent learning awareness"

Connected through hyperedge HE-7 (induced by source paper "multi step retriever reader interaction for scalable open domain question answering"):
  with members:
    - "training a ranking function for open domain question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "efficient and robust question answering from minimal context over documents"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "fast k nearest neighbour search via prioritized dci"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-8 (induced by source paper "xoc explainable observer classifier for explainable binary decisions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "full gradient representation for neural network visualization"
    - "neural network attributions a causal perspective"
    - "cxplain causal explanations for model interpretation under uncertainty"
    - "learning attentional communication for multi agent cooperation"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "tarmac targeted multi agent communication"

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.AI(Artificial Intelligence)