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: "learning existing social conventions via observationally augmented self play"
  Abstract: In order for artificial agents to coordinate effectively with people, they must act consistently with existing conventions (e.g. how to navigate in traffic, which language to speak, or how to coordinate with teammates). A group's conventions can be viewed as a choice of equilibrium in a coordination game. We consider the problem of an agent learning a policy for a coordination game in a simulated environment and then using this policy when it enters an existing group. When there are multiple possible conventions we show that learning a policy via multi-agent reinforcement learning (MARL) is likely to find policies which achieve high payoffs at training time but fail to coordinate with the real group into which the agent enters. We assume access to a small number of samples of behavior from the true convention and show that we can augment the MARL objective to help it find policies consistent with the real group's convention. In three environments from the literature - traffic, communication, and team coordination - we observe that augmenting MARL with a small amount of imitation learning greatly increases the probability that the strategy found by MARL fits well with the existing social convention. We show that this works even in an environment where standard training methods very rarely find the true convention of the agent's partners.

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 "adversarially guided self play for adopting social conventions"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "a style based generator architecture for generative adversarial networks"
    - "context aware captions from context agnostic supervision"
    - "emergence of grounded compositional language in multi agent populations"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "image to image translation with conditional adversarial networks"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "on solving cooperative marl problems with a few good experiences"):
  with members:
    - "self imitation learning"
    - "counterfactual multi agent policy gradients"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "deep reinforcement learning from self play in imperfect information games"
    - "improved memory bounded dynamic programming for decentralized pomdps"

Connected through hyperedge HE-4 (induced by source paper "on the utility of learning about humans for human ai coordination"):
  with members:
    - "on the utility of model learning in hri"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "population based training of neural networks"

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)