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: "learning transferable cooperative behavior in multi agent teams"
  Abstract: While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box. We propose to create a shared agent-entity graph, where agents and environmental entities form vertices, and edges exist between the vertices which can communicate with each other. Agents learn to cooperate by exchanging messages along the edges of this graph. Our proposed multi-agent reinforcement learning framework is invariant to the number of agents or entities present in the system as well as permutation invariance, both of which are desirable properties for any multi-agent system representation. We present state-of-the-art results on coverage, formation and line control tasks for multi-agent teams in a fully decentralized framework and further show that the learned policies quickly transfer to scenarios with different team sizes along with strong zero-shot generalization performance. This is an important step towards developing multi-agent teams which can be realistically deployed in the real world without assuming complete prior knowledge or instantaneous communication at unbounded distances.

Connected through hyperedge HE-1 (induced by source paper "connectivity driven communication in multi agent reinforcement learning through diffusion processes on graphs"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "learning nearly decomposable value functions via communication minimization"
    - "stmarl a spatio temporal multi agent reinforcement learning approach for traffic light control"
    - "learning attentional communication for multi agent cooperation"
    - "tarmac targeted multi agent communication"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "actor attention critic for multi agent reinforcement learning"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-2 (induced by source paper "scaling up multiagent reinforcement learning for robotic systems learn an adaptive sparse communication graph"):
  with members:
    - "adaptively sparse transformers"
    - "graph policy gradients for large scale robot control"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"
    - "mean field multi agent reinforcement learning"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "on controllable sparse alternatives to softmax"

Connected through hyperedge HE-3 (induced by source paper "learning when to transfer among agents an efficient multiagent transfer learning framework"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "learning to teach in cooperative multiagent reinforcement learning"
    - "proximal policy optimization algorithms"
    - "the option critic architecture"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "improving sat solver heuristics with graph networks and reinforcement learning"):
  with members:
    - "reinforcement learning driven heuristic optimization"
    - "guiding high performance sat solvers with unsat core predictions"
    - "graph representations for higher order logic and theorem proving"
    - "fast graph representation learning with pytorch geometric"
    - "structured agents for physical construction"
    - "deep multi agent reinforcement learning with relevance graphs"
    - "learning a sat solver from single bit supervision"
    - "graph networks as learnable physics engines for inference and control"

Connected through hyperedge HE-5 (induced by source paper "from few to more large scale dynamic multiagent curriculum learning"):
  with members:
    - "the starcraft multi agent challenge"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "factorized q learning for large scale multi agent systems"
    - "learning curriculum policies for reinforcement learning"
    - "how powerful are graph neural networks"
    - "deep q learning from demonstrations"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "relational graph learning for crowd navigation"):
  with members:
    - "lets drive driving in a crowd by learning from tree search"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "sparse interacting gaussian processes efficiency and optimality theorems of autonomous crowd navigation"
    - "attention is all you need"
    - "vain attentional multi agent predictive modeling"
    - "a structured self attentive sentence embedding"
    - "the predictron end to end learning and planning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "graph convolutional reinforcement learning"):
  with members:
    - "value propagation for decentralized networked deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "tarmac targeted multi agent communication"
    - "deep multi agent reinforcement learning with relevance graphs"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "relational deep reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "magent a many agent reinforcement learning platform for artificial collective intelligence"

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)