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: "social learning strategies modify the effect of network structure on group performance"
  Abstract: Previous studies have disagreed over whether efficient or inefficient network structures should be more effective in promoting group performance. Here, Barkoczi and Galesic demonstrate that which structure is superior depends on the social learning strategy used by individuals in the network.

Connected through hyperedge HE-1 (induced by source paper "leveraging communication topologies between learning agents in deep reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "diff dac distributed actor critic for multitask deep reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "massively parallel methods for deep reinforcement learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari with deep reinforcement learning"
    - "an introduction to collective intelligence"

Connected through hyperedge HE-2 (induced by source paper "communication topologies between learning agents in deep reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "diff dac distributed actor critic for multitask deep reinforcement learning"
    - "population based training of neural networks"
    - "asynchronous methods for deep reinforcement learning"
    - "massively parallel methods for deep reinforcement learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "network structure efficiency and performance in wikiprojects"):
  with members:
    - "laboratories of oligarchy how the iron law extends to peer production"
    - "cooperation and contagion in web based networked public goods experiments"

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.SI(Social and Information Networks)