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: "an introduction to collective intelligence"
  Abstract: This paper surveys the emerging science of how to design a “COllective INtelligence” (COIN). A COIN is a large multi-agent system where: i) There is little to no centralized communication or control. ii) There is a provided world utility function that rates the possible histories of the full system. In particular, we are interested in COINs in which each agent runs a reinforcement learning (RL) algorithm. The conventional approach to designing large distributed systems to optimize a world utility does not use agents running RL algorithms. Rather, that approach begins with explicit modeling of the dynamics of the overall system, followed by detailed hand-tuning of the interactions between the components to ensure that they “cooperate” as far as the world utility is concerned. This approach is labor-intensive, often results in highly nonrobust systems, and usually results in design techniques that have limited applicability. In contrast, we wish to solve the COIN design problem implicitly, via the “adaptive” character of the RL algorithms of each of the agents. This approach introduces an entirely new, profound design problem: Assuming the RL algorithms are able to achieve high rewards, what reward functions for the individual agents will, when pursued by those agents, result in high world utility? In other words, what reward functions will best ensure that we do not have phenomena like the tragedy of the commons, Braess’s paradox, or the liquidity trap? Although still very young, research specifically concentrating on the COIN design problem has already resulted in successes in artificial domains, in particular in packet-routing, the leader-follower problem, and in variants of Arthur’s El Farol bar problem. It is expected that as it matures and draws upon other disciplines related to COINs, this research will greatly expand the range of tasks addressable by human engineers. Moreover, in addition to drawing on them, such a fully developed science of COIN design may provide much insight into other already established scientific fields, such as economics, game theory, and population biology.

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"
    - "social learning strategies modify the effect of network structure on group performance"
    - "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-2 (induced by source paper "human level performance in 3d multiplayer games with population based reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning with opponent learning awareness"
    - "feudal networks for hierarchical reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "emergence of grounded compositional language in multi agent populations"
    - "population based training of neural networks"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (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"
    - "consequentialist conditional cooperation in social dilemmas with imperfect information"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "embodied evolution in collective robotics a review"):
  with members:
    - "robots that can adapt like animals"
    - "planning for decentralized control of multiple robots under uncertainty"
    - "comparison of selection methods in on line distributed evolutionary robotics"

Connected through hyperedge HE-5 (induced by source paper "avoiding braess paradox through collective intelligence"):
  with members:
    - "general principles of learning based multi agent systems"
    - "using collective intelligence to route internet traffic"
    - "reinforcement learning a survey"

Connected through hyperedge HE-6 (induced by source paper "collective intelligence data routing and braess paradox"):
  with members:
    - "collective intelligence for control of distributed dynamical systems"
    - "using collective intelligence to route internet traffic"
    - "adaptivity in agent based routing for data networks"
    - "anytime coalition structure generation with worst case guarantees"
    - "reinforcement learning a survey"
    - "adaptive load balancing a study in multi agent learning"

Connected through hyperedge HE-7 (induced by source paper "collective intelligence for control of distributed dynamical systems"):
  with members:
    - "using collective intelligence to route internet traffic"

Connected through hyperedge HE-8 (induced by source paper "adaptivity in agent based routing for data networks"):
  with members:
    - "avoiding braess paradox through collective intelligence"
    - "general principles of learning based multi agent systems"
    - "using collective intelligence to route internet traffic"

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.LG(Machine Learning)