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: "search on the replay buffer bridging planning and reinforcement learning"
  Abstract: The history of learning for control has been an exciting back and forth between two broad classes of algorithms: planning and reinforcement learning. Planning algorithms effectively reason over long horizons, but assume access to a local policy and distance metric over collision-free paths. Reinforcement learning excels at learning policies and the relative values of states, but fails to plan over long horizons. Despite the successes of each method in various domains, tasks that require reasoning over long horizons with limited feedback and high-dimensional observations remain exceedingly challenging for both planning and reinforcement learning algorithms. Frustratingly, these sorts of tasks are potentially the most useful, as they are simple to design (a human only need to provide an example goal state) and avoid reward shaping, which can bias the agent towards finding a sub-optimal solution. We introduce a general control algorithm that combines the strengths of planning and reinforcement learning to effectively solve these tasks. Our aim is to decompose the task of reaching a distant goal state into a sequence of easier tasks, each of which corresponds to reaching a subgoal. Planning algorithms can automatically find these waypoints, but only if provided with suitable abstractions of the environment -- namely, a graph consisting of nodes and edges. Our main insight is that this graph can be constructed via reinforcement learning, where a goal-conditioned value function provides edge weights, and nodes are taken to be previously seen observations in a replay buffer. Using graph search over our replay buffer, we can automatically generate this sequence of subgoals, even in image-based environments. Our algorithm, search on the replay buffer (SoRB), enables agents to solve sparse reward tasks over one hundred steps, and generalizes substantially better than standard RL algorithms.

Connected through hyperedge HE-1 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement"):
  with members:
    - "gradient surgery for multi task learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "learning to reach goals without reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "learning latent plans from play"
    - "advantage weighted regression simple and scalable off policy reinforcement learning"
    - "deepmind control suite"
    - "reinforcement learning and control as probabilistic inference tutorial and review"

Connected through hyperedge HE-2 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks"):
  with members:
    - "planning with goal conditioned policies"
    - "benchmarking model based reinforcement learning"
    - "universal planning networks"
    - "deepmind control suite"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "value propagation networks"
    - "learning multi level hierarchies with hindsight"
    - "hindsight experience replay"

Connected through hyperedge HE-3 (induced by source paper "sparse graphical memory for robust planning"):
  with members:
    - "learning world graphs to accelerate hierarchical reinforcement learning"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "mapping state space using landmarks for universal goal reaching"
    - "hallucinative topological memory for zero shot visual planning"
    - "temporal difference models model free deep rl for model based control"
    - "visual reinforcement learning with imagined goals"
    - "distributed distributional deterministic policy gradients"
    - "hindsight experience replay"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning with goal distance gradient"):
  with members:
    - "deep multi agent reinforcement learning with discrete continuous hybrid action spaces"
    - "visual reinforcement learning with imagined goals"
    - "addressing function approximation error in actor critic methods"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "hindsight experience replay"
    - "deep learning for reward design to improve monte carlo tree search in atari games"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-5 (induced by source paper "adjust planning strategies to accommodate reinforcement learning agents"):
  with members:
    - "semi parametric topological memory for navigation"
    - "a distributional perspective on reinforcement learning"
    - "prm rl long range robotic navigation tasks by combining reinforcement learning and sampling based planning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "value iteration networks"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "multi task reinforcement learning with a planning quasi metric"):
  with members:
    - "mastering atari go chess and shogi by planning with a learned model"
    - "actor mimic deep multitask and transfer reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "learning world graphs to accelerate hierarchical reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "world discovery models"
    - "hierarchical policy learning is sensitive to goal space design"
    - "interpretable neural predictions with differentiable binary variables"
    - "model based reinforcement learning for atari"
    - "discovering options for exploration by minimizing cover time"
    - "keyin discovering subgoal structure with keyframe based video prediction"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-8 (induced by source paper "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"):
  with members:
    - "learning robotic manipulation through visual planning and acting"
    - "learning latent plans from play"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "robot motion planning in learned latent spaces"
    - "hierarchical reinforcement learning with hindsight"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "data efficient hierarchical 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)