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: "inequity aversion improves cooperation in intertemporal social dilemmas"
  Abstract: Groups of humans are often able to find ways to cooperate with one another in complex, temporally extended social dilemmas. Models based on behavioral economics are only able to explain this phenomenon for unrealistic stateless matrix games. Recently, multi-agent reinforcement learning has been applied to generalize social dilemma problems to temporally and spatially extended Markov games. However, this has not yet generated an agent that learns to cooperate in social dilemmas as humans do. A key insight is that many, but not all, human individuals have inequity averse social preferences. This promotes a particular resolution of the matrix game social dilemma wherein inequity-averse individuals are personally pro-social and punish defectors. Here we extend this idea to Markov games and show that it promotes cooperation in several types of sequential social dilemma, via a profitable interaction with policy learnability. In particular, we find that inequity aversion improves temporal credit assignment for the important class of intertemporal social dilemmas. These results help explain how large-scale cooperation may emerge and persist.

Connected through hyperedge HE-1 (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"
    - "m 3 rl mind aware multi agent management reinforcement learning"
    - "learning policy representations in multiagent systems"

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"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "evolving intrinsic motivations for altruistic behavior"
    - "representation learning with contrastive predictive coding"

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"
    - "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"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"

Connected through hyperedge HE-4 (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-5 (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"
    - "evolving intrinsic motivations for altruistic behavior"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "machine theory of mind"
    - "a deep policy inference q network for multi agent systems"

Connected through hyperedge HE-6 (induced by source paper "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"
    - "a generalised method for empirical game theoretic analysis"
    - "episodic curiosity through reachability"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "unsupervised predictive memory in a goal directed agent"
    - "malthusian reinforcement learning"

Connected through hyperedge HE-7 (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"
    - "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"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-8 (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"
    - "learning existing social conventions via observationally augmented self play"
    - "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.NE(Neural and Evolutionary Computing)