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: "coordinated exploration in concurrent reinforcement learning"
  Abstract: We consider a team of reinforcement learning agents that concurrently learn to operate in a common environment. We identify three properties - adaptivity, commitment, and diversity - which are necessary for efficient coordinated exploration and demonstrate that straightforward extensions to single-agent optimistic and posterior sampling approaches fail to satisfy them. As an alternative, we propose seed sampling, which extends posterior sampling in a manner that meets these requirements. Simulation results investigate how per-agent regret decreases as the number of agents grows, establishing substantial advantages of seed sampling over alternative exploration schemes.

Connected through hyperedge HE-1 (induced by source paper "population guided parallel policy search for reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "scalable coordinated exploration in concurrent reinforcement learning"
    - "self imitation learning"
    - "evolution guided policy gradient in reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning in healthcare a survey"):
  with members:
    - "does the artificial intelligence clinician learn optimal treatment strategies for sepsis in intensive care"
    - "a dual mode adaptive basal bolus advisor based on reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "improving sepsis treatment strategies by combining deep and kernel based reinforcement learning"
    - "partial policy based reinforcement learning for anatomical landmark localization in 3d medical images"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "automatic view planning with multi scale deep reinforcement learning agents"
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"

Connected through hyperedge HE-3 (induced by source paper "efficient exploration via state marginal matching"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "robel robotics benchmarks for learning with low cost robots"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "concurrent meta reinforcement learning"
    - "benchmarking bonus based exploration methods on the arcade learning environment"
    - "provably efficient maximum entropy exploration"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "exploration by random network distillation"

Connected through hyperedge HE-4 (induced by source paper "influence based multi agent exploration"):
  with members:
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "coordinated exploration via intrinsic rewards for multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "exploration by random network distillation"
    - "learning to share and hide intentions using information regularization"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "actor attention critic for multi agent reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "maven multi agent variational exploration"):
  with members:
    - "the starcraft multi agent challenge"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "cesma centralized expert supervises multi agents"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "virel a variational inference framework for reinforcement learning"
    - "new and surprising ways to be mean adversarial npcs with coupled empowerment minimisation"
    - "latent space policies for hierarchical reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "concurrent meta reinforcement learning"):
  with members:
    - "some considerations on learning to explore via meta reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "unsupervised meta learning for reinforcement learning"
    - "scalable coordinated exploration in concurrent reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "vain attentional multi agent predictive modeling"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"

Connected through hyperedge HE-7 (induced by source paper "pac guarantees for cooperative multi agent reinforcement learning with restricted communication"):
  with members:
    - "concurrent meta reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "scalable coordinated exploration in concurrent reinforcement learning"
    - "decentralized cooperative stochastic multi armed bandits"
    - "emergent complexity via multi agent competition"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "distributed clustering of linear bandits in peer to peer networks"

Connected through hyperedge HE-8 (induced by source paper "diverse exploration via conjugate policies for policy gradient methods"):
  with members:
    - "diverse exploration for fast and safe policy improvement"
    - "parameter space noise for exploration"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"
    - "noisy networks for exploration"
    - "benchmarking deep reinforcement learning for continuous control"
    - "trust region policy optimization"

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.AI(Artificial Intelligence)