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: "loss aversion fosters coordination among independent reinforcement learners"
  Abstract: We study what are the factors that can accelerate the emergence of collaborative behaviours among independent selfish learning agents. We depart from the "Battle of the Exes" (BoE), a spatial repeated game from which human behavioral data has been obtained (by Hawkings and Goldstone, 2016) that we find interesting because it considers two cases: a classic game theory version, called ballistic, in which agents can only make one action/decision (equivalent to the Battle of the Sexes) and a spatial version, called dynamic, in which agents can change decision (a spatial continuous version). We model both versions of the game with independent reinforcement learning agents and we manipulate the reward function transforming it into an utility introducing "loss aversion": the reward that an agent obtains can be perceived as less valuable when compared to what the other got. We prove experimentally the introduction of loss aversion fosters cooperation by accelerating its appearance, and by making it possible in some cases like in the dynamic condition. We suggest that this may be an important factor explaining the rapid converge of human behaviour towards collaboration reported in the experiment of Hawkings and Goldstone.

Connected through hyperedge HE-1 (induced by source paper "modeling theory of mind in multi agent games using adaptive feedback control"):
  with members:
    - "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"
    - "consequentialist conditional cooperation in social dilemmas with imperfect information"

Connected through hyperedge HE-2 (induced by source paper "individual specialization in multi task environments with multiagent reinforcement learners"):
  with members:
    - "modeling the formation of social conventions in multi agent populations"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "asynchronous methods for deep reinforcement learning"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q 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)