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: "concurrent meta reinforcement learning"
  Abstract: State-of-the-art meta reinforcement learning algorithms typically assume the setting of a single agent interacting with its environment in a sequential manner. A negative side-effect of this sequential execution paradigm is that, as the environment becomes more and more challenging, and thus requiring more interaction episodes for the meta-learner, it needs the agent to reason over longer and longer time-scales. To combat the difficulty of long time-scale credit assignment, we propose an alternative parallel framework, which we name "Concurrent Meta-Reinforcement Learning" (CMRL), that transforms the temporal credit assignment problem into a multi-agent reinforcement learning one. In this multi-agent setting, a set of parallel agents are executed in the same environment and each of these "rollout" agents are given the means to communicate with each other. The goal of the communication is to coordinate, in a collaborative manner, the most efficient exploration of the shared task the agents are currently assigned. This coordination therefore represents the meta-learning aspect of the framework, as each agent can be assigned or assign itself a particular section of the current task's state space. This framework is in contrast to standard RL methods that assume that each parallel rollout occurs independently, which can potentially waste computation if many of the rollouts end up sampling the same part of the state space. Furthermore, the parallel setting enables us to define several reward sharing functions and auxiliary losses that are non-trivial to apply in the sequential setting. We demonstrate the effectiveness of our proposed CMRL at improving over sequential methods in a variety of challenging tasks.

Connected through hyperedge HE-1 (induced by source paper "represented value function approach for large scale multi agent reinforcement learning"):
  with members:
    - "graph policy gradients for large scale robot control"
    - "stability properties of graph neural networks"
    - "actor attention critic for multi agent reinforcement learning"
    - "mean field multi agent reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-2 (induced by source paper "solving rubik s cube with a robot hand"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "active domain randomization"
    - "meta reinforcement learning with task embedding and shared policy"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "distilling policy distillation"
    - "deep dynamics models for learning dexterous manipulation"
    - "meta reinforcement learning as task inference"
    - "deceptionnet network driven domain randomization"

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"
    - "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"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-4 (induced by source paper "graph policy gradients for large scale robot control"):
  with members:
    - "stability properties of graph neural networks"
    - "a comprehensive survey on graph neural networks"
    - "scalable centralized deep multi agent reinforcement learning via policy gradients"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "counterfactual multi agent policy gradients"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "memory augmented control networks"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-5 (induced by source paper "pac guarantees for cooperative multi agent reinforcement learning with restricted communication"):
  with members:
    - "learning attentional communication for multi agent cooperation"
    - "scalable coordinated exploration in concurrent reinforcement learning"
    - "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-6 (induced by source paper "pac guarantees for concurrent reinforcement learning with restricted communication"):
  with members:
    - "scalable coordinated exploration in concurrent reinforcement learning"
    - "coordinated exploration in concurrent reinforcement learning"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "learning multiagent communication with backpropagation"

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)