human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cm3 cooperative multi goal multi stage multi agent reinforcement learning"):
  Abstract: A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient exploration for learning both individual goal attainment and cooperation for others' success, and credit-assignment for interactions between actions and goals of different agents. To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for localized credit assignment. We use a function augmentation scheme to bridge value and policy functions across the curriculum. The complete architecture, called CM3, learns significantly faster than direct adaptations of existing algorithms on three challenging multi-goal multi-agent problems: cooperative navigation in difficult formations, negotiating multi-vehicle lane changes in the SUMO traffic simulator, and strategic cooperation in a Checkers environment.

Includes members:
  Member "integrating independent and centralized multi agent reinforcement learning for traffic signal network optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical cooperative multi agent reinforcement learning with skill discovery")
  Member "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi agent reinforcement learning with emergent roles")
  Member "emergent coordination through competition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "human level performance in 3d multiplayer games with population based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "learning when to communicate at scale in multiagent cooperative and competitive tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")
  Member "variance reduction for policy gradient with action dependent factorized baselines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for cyber security")
  Member "m 3 rl mind aware multi agent management reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cluster based social reinforcement learning")
  Member "efficient large scale fleet management via multi agent deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reward design for driver repositioning using multi agent reinforcement learning")

Question: Please tell me which class the source paper 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)