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: "consequentialist conditional cooperation in social dilemmas with imperfect information"
  Abstract: Social dilemmas, where mutual cooperation can lead to high payoffs but participants face incentives to cheat, are ubiquitous in multi-agent interaction. We wish to construct agents that cooperate with pure cooperators, avoid exploitation by pure defectors, and incentivize cooperation from the rest. However, often the actions taken by a partner are (partially) unobserved or the consequences of individual actions are hard to predict. We show that in a large class of games good strategies can be constructed by conditioning one's behavior solely on outcomes (ie. one's past rewards). We call this consequentialist conditional cooperation. We show how to construct such strategies using deep reinforcement learning techniques and demonstrate, both analytically and experimentally, that they are effective in social dilemmas beyond simple matrix games. We also show the limitations of relying purely on consequences and discuss the need for understanding both the consequences of and the intentions behind an action.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "inducing cooperative behaviour in sequential social dilemmas through multi agent reinforcement learning using status quo loss"):
  with members:
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "no press diplomacy modeling multi agent gameplay"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "emergent communication through negotiation"
    - "learning with opponent learning awareness"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "multi agent reinforcement learning in sequential social dilemmas"

Connected through hyperedge HE-5 (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"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "a unified game theoretic approach to multiagent reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "learning reciprocity in complex sequential social dilemmas"):
  with members:
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "relational forward models for multi agent learning"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "asynchronous methods for deep reinforcement learning"
    - "interaction networks for learning about objects relations and physics"

Connected through hyperedge HE-7 (induced by source paper "foolproof cooperative learning"):
  with members:
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "learning with opponent learning awareness"
    - "robust learning equilibrium"
    - "a polynomial time nash equilibrium algorithm for repeated stochastic games"

Connected through hyperedge HE-8 (induced by source paper "backplay man muss immer umkehren"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "forward backward reinforcement learning"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "barc backward reachability curriculum for robotic reinforcement learning"
    - "pommerman a multi agent playground"
    - "kickstarting deep reinforcement learning"

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)