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 "multi agent collaboration for feasible collaborative behavior construction and evaluation"):
  Abstract: In the case of the two-person zero-sum stochastic game with a central controller, this paper proposes a best collaborative behavior search and selection algorithm based on reinforcement learning, in response to how to choose the best collaborative object and action for the central controller. In view of the existing multi-agent collaboration and confrontation reinforcement learning methods, the methods of traversing all actions in a certain state leads to the problem of long calculation time and unsafe policy exploration. This paper proposes to construct a feasible collaborative behavior set by using action space discretization, establishing models of both sides, model-based prediction and parallel search. Then, we use the deep q-learning method in reinforcement learning to train the scoring function to select the optimal collaboration behavior from the feasible collaborative behavior set. This method enables efficient and accurate calculation in an environment with strong confrontation, high dynamics and a large number of agents, which is verified by the RoboCup Small Size League robots passing collaboration.

Includes members:
  Member "deep multi agent reinforcement learning with discrete continuous hybrid action spaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning with goal distance gradient")
  Member "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi agent reinforcement learning with emergent roles")
  Member "multi agent actor critic for mixed cooperative competitive environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "learning to communicate with deep multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "learning multiagent communication with backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.RO(Robotics)