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 "cooperation without coordination hierarchical predictive planning for decentralized multiagent navigation"):
  Abstract: Decentralized multiagent planning raises many challenges, such as adaption to changing environments inexplicable by the agent's own behavior, coordination from noisy sensor inputs like lidar, cooperation without knowing other agents' intents. To address these challenges, we present hierarchical predictive planning (HPP) for decentralized multiagent navigation tasks. HPP learns prediction models for itself and other teammates, and uses the prediction models to propose and evaluate navigation goals that complete the cooperative task without explicit coordination. To learn the prediction models, HPP observes other agents' behavior and learns to maps own sensors to predicted locations of other agents. HPP then uses the cross-entropy method to iteratively propose, evaluate, and improve navigation goals, under assumption that all agents in the team share a common objective. HPP removes the need for a centralized operator (i.e. robots determine their own actions without coordinating their beliefs or plans) and can be trained and easily transferred to real world environments. The results show that HPP generalizes to new environments including real-world robot team. It is also 33x more sample efficient and performs better in complex environments compared to a baseline. The video and website for this paper can be found at this https URL and this https URL.

Includes members:
  Member "long range indoor navigation with prm rl":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "zero shot imitation learning from demonstrations for legged robot visual navigation")
  Member "r maddpg for partially observable environments and limited communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep multi agent reinforcement learning for decentralized continuous cooperative control")
  Member "rl rrt kinodynamic motion planning via learning reachability estimators from rl policies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reinforcement learning with probabilistically complete exploration")
  Member "actor attention critic for multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")
  Member "learning navigation behaviors end to end with autorl":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning curiosity algorithms")
  Member "getting robots unfrozen and unlost in dense pedestrian crowds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors")
  Member "efficient hierarchical robot motion planning under uncertainty and hybrid dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning hybrid object kinematics for efficient hierarchical planning under uncertainty")
  Member "counterfactual multi agent policy gradients":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")

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.MA(Multiagent Systems)