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: "prosocial learning agents solve generalized stag hunts better than selfish ones"
  Abstract: Deep reinforcement learning has become an important paradigm for constructing agents that can enter complex multi-agent situations and improve their policies through experience. One commonly used technique is reactive training - applying standard RL methods while treating other agents as a part of the learner's environment. It is known that in general-sum games reactive training can lead groups of agents to converge to inefficient outcomes. We focus on one such class of environments: Stag Hunt games. Here agents either choose a risky cooperative policy (which leads to high payoffs if both choose it but low payoffs to an agent who attempts it alone) or a safe one (which leads to a safe payoff no matter what). We ask how we can change the learning rule of a single agent to improve its outcomes in Stag Hunts that include other reactive learners. We extend existing work on reward-shaping in multi-agent reinforcement learning and show that that making a single agent prosocial, that is, making them care about the rewards of their partners can increase the probability that groups converge to good outcomes. Thus, even if we control a single agent in a group making that agent prosocial can increase our agent's long-run payoff. We show experimentally that this result carries over to a variety of more complex environments with Stag Hunt-like dynamics including ones where agents must learn from raw input pixels.

Connected through hyperedge HE-1 (induced by source paper "other play for zero shot coordination"):
  with members:
    - "adversarially guided self play for adopting social conventions"
    - "plannable approximations to mdp homomorphisms equivariance under actions"
    - "the hanabi challenge a new frontier for ai research"
    - "improving policies via search in cooperative partially observable games"
    - "on the utility of learning about humans for human ai coordination"
    - "to populate is to regulate"
    - "simplified action decoder for deep multi agent reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "social diversity and social preferences in mixed motive reinforcement learning"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "on the utility of learning about humans for human ai coordination"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "evolving intrinsic motivations for altruistic behavior"

Connected through hyperedge HE-3 (induced by source paper "silly rules improve the capacity of agents to learn stable enforcement and compliance behaviors"):
  with members:
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "legible normativity for ai alignment the value of silly rules"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "evolving intrinsic motivations for altruistic behavior"
    - "representation learning with contrastive predictive coding"
    - "a multi agent reinforcement learning model of common pool resource appropriation"

Connected through hyperedge HE-4 (induced by source paper "multi agent reinforcement learning and human social factors in climate change mitigation"):
  with members:
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "cooperating with machines"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "affect control processes"
    - "stance and sentiment in tweets"
    - "multiagent cooperation and competition with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "influence based multi agent exploration"):
  with members:
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "coordinated exploration via intrinsic rewards for multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "exploration by random network distillation"
    - "learning to share and hide intentions using information regularization"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "actor attention critic for multi agent reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "learning fairness in multi agent systems"):
  with members:
    - "a cooperative multi agent reinforcement learning framework for resource balancing in complex logistics network"
    - "the matthew effect in computation contests high difficulty may lead to 51 dominance"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "evolving intrinsic motivations for altruistic behavior"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "machine theory of mind"

Connected through hyperedge HE-7 (induced by source paper "theory of minds understanding behavior in groups through inverse planning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "machine theory of mind"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "learning with opponent learning awareness"
    - "multi agent reinforcement learning in sequential social dilemmas"

Connected through hyperedge HE-8 (induced by source paper "modeling theory of mind in multi agent games using adaptive feedback control"):
  with members:
    - "loss aversion fosters coordination among independent reinforcement learners"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "modeling the formation of social conventions in multi agent populations"
    - "machine theory of mind"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "consequentialist conditional cooperation in social dilemmas with imperfect information"

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)