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: "playing atari games with deep reinforcement learning and human checkpoint replay"
  Abstract: This paper introduces a novel method for learning how to play the most difficult Atari 2600 games from the Arcade Learning Environment using deep reinforcement learning. The proposed method, human checkpoint replay, consists in using checkpoints sampled from human gameplay as starting points for the learning process. This is meant to compensate for the difficulties of current exploration strategies, such as epsilon-greedy, to find successful control policies in games with sparse rewards. Like other deep reinforcement learning architectures, our model uses a convolutional neural network that receives only raw pixel inputs to estimate the state value function. We tested our method on Montezuma's Revenge and Private Eye, two of the most challenging games from the Atari platform. The results we obtained show a substantial improvement compared to previous learning approaches, as well as over a random player. We also propose a method for training deep reinforcement learning agents using human gameplay experience, which we call human experience replay.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "an optimization framework for task sequencing in curriculum learning"
    - "curriculum learning for cumulative return maximization"
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "learning curriculum policies for reinforcement learning"
    - "screenernet learning self paced curriculum for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "certified reinforcement learning with logic guidance"):
  with members:
    - "the probabilistic model checker storm"
    - "deepsynth program synthesis for automatic task segmentation in deep reinforcement learning"
    - "rethinking the discount factor in reinforcement learning a decision theoretic approach"
    - "verifiably safe off model reinforcement learning"
    - "logically constrained reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "from game theoretic multi agent log linear learning to reinforcement learning"
    - "observe and look further achieving consistent performance on atari"

Connected through hyperedge HE-3 (induced by source paper "hybrid reinforcement learning with expert state sequences"):
  with members:
    - "playing hard exploration games by watching youtube"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "behavioral cloning from observation"
    - "eigenoption discovery through the deep successor representation"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "visual question generation as dual task of visual question answering"
    - "observational learning by reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration"):
  with members:
    - "sample efficient adversarial imitation learning from observation"
    - "imitation learning from video by leveraging proprioception"
    - "recent advances in imitation learning from observation"
    - "learning actionable representations from visual observations"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "generative adversarial imitation from observation"
    - "behavioral cloning from observation"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-5 (induced by source paper "which channel to ask my question personalized customer service requeststream routing using deepreinforcement learning"):
  with members:
    - "the mirage of action dependent baselines in reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "a long short term memory recurrent neural network framework for network traffic matrix prediction"
    - "proximal policy optimization algorithms"
    - "a machine learning framework for resource allocation assisted by cloud computing"

Connected through hyperedge HE-6 (induced by source paper "one shot high fidelity imitation training large scale deep nets with rl"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"

Connected through hyperedge HE-7 (induced by source paper "backplay man muss immer umkehren"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "forward backward reinforcement learning"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "barc backward reachability curriculum for robotic reinforcement learning"
    - "pommerman a multi agent playground"
    - "kickstarting deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "learning montezuma s revenge from a single demonstration"):
  with members:
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "expert augmented actor critic for vizdoom and montezumas revenge"
    - "observe and look further achieving consistent performance on atari"
    - "equivalence between policy gradients and soft q learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

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)