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: "planning by prioritized sweeping with small backups"
  Abstract: Efficient planning plays a crucial role in model-based reinforcement learning. Traditionally, the main planning operation is a full backup based on the current estimates of the successor states. Consequently, its computation time is proportional to the number of successor states. In this paper, we introduce a new planning backup that uses only the current value of a single successor state and has a computation time independent of the number of successor states. This new backup, which we call a small backup, opens the door to a new class of model-based reinforcement learning methods that exhibit much finer control over their planning process than traditional methods. We empirically demonstrate that this increased flexibility allows for more efficient planning by showing that an implementation of prioritized sweeping based on small backups achieves a substantial performance improvement over classical implementations.

Connected through hyperedge HE-1 (induced by source paper "tight regret bounds for model based reinforcement learning with greedy policies"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "learning latent dynamics for planning from pixels"
    - "integrating planning for task completion dialogue policy learning"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "is q learning provably efficient"
    - "policy certificates towards accountable reinforcement learning"
    - "how to combine tree search methods in reinforcement learning"
    - "feedback based tree search for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "prioritizing starting states for reinforcement learning"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "self imitation learning"
    - "sim to real learning agile locomotion for quadruped robots"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "learning montezuma s revenge from a single demonstration"
    - "parameter space noise for exploration"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "experience replay using transition sequences"):
  with members:
    - "selective experience replay for lifelong learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "hindsight experience replay"
    - "identification and off policy learning of multiple objectives using adaptive clustering"
    - "swarm enabling technology for multi robot systems"
    - "sample efficient actor critic with experience replay"
    - "asynchronous methods for deep reinforcement learning"
    - "data efficient off policy policy evaluation for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "playing atari games with deep reinforcement learning and human checkpoint replay"):
  with members:
    - "deep exploration via bootstrapped dqn"
    - "prioritized experience replay"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "massively parallel methods for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "prioritized experience replay"):
  with members:
    - "increasing the action gap new operators for reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "incentivizing exploration in reinforcement learning with deep predictive models"
    - "massively parallel methods for deep reinforcement learning"
    - "adam a method for stochastic optimization"
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari with deep reinforcement learning"

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)