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: "using collective intelligence to route internet traffic"
  Abstract: A COllective INtelligence (COIN) is a set of interacting reinforcement learning (RL) algorithms designed in an automated fashion so that their collective behavior optimizes a global utility function. We summarize the theory of COINs, then present experiments using that theory to design COINs to control internet traffic routing. These experiments indicate that COINs outperform all previously investigated RL-based, shortest path routing algorithms.

Connected through hyperedge HE-1 (induced by source paper "reward design in cooperative multi agent reinforcement learning for packet routing"):
  with members:
    - "learning agent communication under limited bandwidth by message pruning"
    - "neighborhood cognition consistent multi agent reinforcement learning"
    - "learning multi agent communication under limited bandwidth restriction for internet packet routing"
    - "modelling the dynamic joint policy of teammates with attention multi agent ddpg"
    - "counterfactual multi agent policy gradients"
    - "a policy search method for temporal logic specified reinforcement learning tasks"
    - "analysing congestion problems in multi agent reinforcement learning"
    - "reinforcement mechanism design for e commerce"

Connected through hyperedge HE-2 (induced by source paper "are multiagent systems resilient to communication failures"):
  with members:
    - "fast convergence in semi anonymous potential games"
    - "potential games are necessary to ensure pure nash equilibria in cost sharing games"
    - "dynamics in near potential games"

Connected through hyperedge HE-3 (induced by source paper "avoiding braess paradox through collective intelligence"):
  with members:
    - "general principles of learning based multi agent systems"
    - "an introduction to collective intelligence"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning for adaptive routing"):
  with members:
    - "learning to cooperate via policy search"
    - "learning finite state controllers for partially observable environments"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "distributed constrained optimization with semicoordinate transformations"):
  with members:
    - "distributed control by lagrangian steepest descent"
    - "collective intelligence data routing and braess paradox"
    - "collective intelligence for control of distributed dynamical systems"

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"
    - "an introduction to collective intelligence"
    - "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:
    - "an introduction to collective intelligence"

Connected through hyperedge HE-8 (induced by source paper "an introduction to collective intelligence"):
  with members:
    - "learning conventions in multiagent stochastic domains using likelihood estimates"
    - "avoiding braess paradox through collective intelligence"
    - "general principles of learning based multi agent systems"
    - "anytime coalition structure generation with worst case guarantees"
    - "reinforcement learning a survey"
    - "adaptive load balancing a study in multi agent learning"
    - "a market oriented programming environment and its application to distributed multicommodity flow problems"

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)