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: "organizing experience a deeper look at replay mechanisms for sample based planning in continuous state domains"
  Abstract: Model-based strategies for control are critical to obtain sample efficient learning. Dyna is a planning paradigm that naturally interleaves learning and planning, by simulating one-step experience to update the action-value function. This elegant planning strategy has been mostly explored in the tabular setting. The aim of this paper is to revisit sample-based planning, in stochastic and continuous domains with learned models. We first highlight the flexibility afforded by a model over Experience Replay (ER). Replay-based methods can be seen as stochastic planning methods that repeatedly sample from a buffer of recent agent-environment interactions and perform updates to improve data efficiency. We show that a model, as opposed to a replay buffer, is particularly useful for specifying which states to sample from during planning, such as predecessor states that propagate information in reverse from a state more quickly. We introduce a semi-parametric model learning approach, called Reweighted Experience Models (REMs), that makes it simple to sample next states or predecessors. We demonstrate that REM-Dyna exhibits similar advantages over replay-based methods in learning in continuous state problems, and that the performance gap grows when moving to stochastic domains, of increasing size.

Connected through hyperedge HE-1 (induced by source paper "an optimistic perspective on offline reinforcement learning"):
  with members:
    - "keep doing what worked behavioral modelling priors for offline reinforcement learning"
    - "benchmarking batch deep reinforcement learning algorithms"
    - "off policy policy gradient with state distribution correction"
    - "behavior regularized offline reinforcement learning"
    - "when to use parametric models in reinforcement learning"
    - "challenges of real world reinforcement learning"
    - "model based reinforcement learning for atari"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"

Connected through hyperedge HE-2 (induced by source paper "frequency based search control in dyna"):
  with members:
    - "hill climbing on value estimates for search control in dyna"
    - "a new perspective on machine learning how to do perfect supervised learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "recurrent world models facilitate policy evolution"
    - "value prediction network"
    - "the predictron end to end learning and planning"
    - "continuous deep q learning with model based acceleration"
    - "self correcting models for model based reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "generative predecessor models for sample efficient imitation learning"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "forward backward reinforcement learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "recall traces backtracking models for efficient reinforcement learning"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "time contrastive networks self supervised learning from video"
    - "density estimation using real nvp"

Connected through hyperedge HE-4 (induced by source paper "experience replay optimization"):
  with members:
    - "remember and forget for experience replay"
    - "meta gradient reinforcement learning"
    - "on learning intrinsic rewards for policy gradient methods"
    - "learning to explore with meta policy gradient"
    - "selective experience replay for lifelong learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "hindsight experience replay"
    - "a deeper look at experience replay"

Connected through hyperedge HE-5 (induced by source paper "intelligent coordination among multiple traffic intersections using multi agent reinforcement learning"):
  with members:
    - "a deep reinforcement learning network for traffic light cycle control"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "deep reinforcement learning that matters"
    - "deep reinforcement learning a brief survey"
    - "adaptive traffic signal control deep reinforcement learning algorithm with experience replay and target network"
    - "using a deep reinforcement learning agent for traffic signal control"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "hill climbing on value estimates for search control in dyna"):
  with members:
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "the effect of planning shape on dyna style planning in high dimensional state spaces"
    - "prioritized experience replay"
    - "dyna style planning with linear function approximation and prioritized sweeping"

Connected through hyperedge HE-7 (induced by source paper "remember and forget for experience replay"):
  with members:
    - "the mirage of action dependent baselines in reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "recall traces backtracking models for efficient reinforcement learning"
    - "selective experience replay for lifelong learning"
    - "deep reinforcement learning that matters"
    - "hindsight experience replay"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"

Connected through hyperedge HE-8 (induced by source paper "the effect of planning shape on dyna style planning in high dimensional state spaces"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "action conditional video prediction using deep networks in atari games"
    - "adam a method for stochastic optimization"
    - "the arcade learning environment an evaluation platform for general agents"
    - "agnostic system identification for model based 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)