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: "integrating independent and centralized multi agent reinforcement learning for traffic signal network optimization"
  Abstract: Traffic congestion in metropolitan areas is a world-wide problem that can be ameliorated by traffic lights that respond dynamically to real-time conditions. Recent studies applying deep reinforcement learning (RL) to optimize single traffic lights have shown significant improvement over conventional control. However, optimization of global traffic condition over a large road network fundamentally is a cooperative multi-agent control problem, for which single-agent RL is not suitable due to environment non-stationarity and infeasibility of optimizing over an exponential joint-action space. Motivated by these challenges, we propose QCOMBO, a simple yet effective multi-agent reinforcement learning (MARL) algorithm that combines the advantages of independent and centralized learning. We ensure scalability by selecting actions from individually optimized utility functions, which are shaped to maximize global performance via a novel consistency regularization loss between individual utility and a global action-value function. Experiments on diverse road topologies and traffic flow conditions in the SUMO traffic simulator show competitive performance of QCOMBO versus recent state-of-the-art MARL algorithms. We further show that policies trained on small sub-networks can effectively generalize to larger networks under different traffic flow conditions, providing empirical evidence for the suitability of MARL for intelligent traffic control.

Connected through hyperedge HE-1 (induced by source paper "hierarchical cooperative multi agent reinforcement learning with skill discovery"):
  with members:
    - "on multi agent learning in team sports games"
    - "winning isn t everything training human like agents for playtesting and game ai"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "google research football a novel reinforcement learning environment"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a survey on traffic signal control methods"):
  with members:
    - "stmarl a spatio temporal multi agent reinforcement learning approach for traffic light control"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "colight learning network level cooperation for traffic signal control"
    - "learning phase competition for traffic signal control"
    - "deep reinforcement learning a brief survey"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "deep deterministic policy gradient for urban traffic light control"
    - "using a deep reinforcement learning agent for traffic signal control"

Connected through hyperedge HE-3 (induced by source paper "cm3 cooperative multi goal multi stage multi agent reinforcement learning"):
  with members:
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "m 3 rl mind aware multi agent management reinforcement learning"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"

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)