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: "review of multi agent algorithms for collective behavior a structural taxonomy"
  Abstract: In this paper, we review multi-agent collective behavior algorithms in the literature and classify them according to their underlying mathematical structure. For each mathematical technique, we identify the multi-agent coordination tasks it can be applied to, and we analyze its scalability, bandwidth use, and demonstrated maturity. We highlight how versatile techniques such as artificial potential functions can be used for applications ranging from low-level position control to high-level coordination and task allocation, we discuss possible reasons for the slow adoption of complex distributed coordination algorithms in the field, and we highlight areas for further research and development.

Connected through hyperedge HE-1 (induced by source paper "context aware deep q network for decentralized cooperative reconnaissance by a robotic swarm"):
  with members:
    - "policy gradient with value function approximation for collective multiagent planning"
    - "learning for multi robot cooperation in partially observable stochastic environments with macro actions"
    - "fully decentralized policies for multi agent systems an information theoretic approach"

Connected through hyperedge HE-2 (induced by source paper "learning scalable and transferable multi robot machine sequential assignment planning via graph embedding"):
  with members:
    - "learning the multiple traveling salesmen problem with permutation invariant pooling networks"
    - "parameter space noise for exploration"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "discriminative embeddings of latent variable models for structured data"
    - "learning modular neural network policies for multi task and multi robot transfer"
    - "continuous control with deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-3 (induced by source paper "graph neural networks for learning robot team coordination"):
  with members:
    - "neural message passing for quantum chemistry"
    - "distributed estimation and control of algebraic connectivity over random graphs"

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.RO(Robotics)