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: "striving for simplicity in off policy deep reinforcement learning"
  Abstract: Reflecting on the advances of off-policy deep reinforcement learning (RL) algorithms since the development of DQN in 2013, it is important to ask: are the complexities of recent off-policy methods really necessary? In an attempt to isolate the contributions of various factors of variation in off-policy deep RL and to help design simpler algorithms, this paper investigates a set of related questions: First, can effective policies be learned given only access to logged offline experience? Second, how much of the benefits of recent distributional RL algorithms is attributed to improvements in exploration versus exploitation behavior? Third, can simpler off-policy RL algorithms outperform distributional RL without learning explicit distributions over returns? This paper uses a batch RL experimental setup on Atari 2600 games to investigate these questions. Unexpectedly, we find that batch RL algorithms trained solely on logged experiences of a DQN agent are able to significantly outperform online DQN. Our experiments suggest that the benefits of distributional RL mainly stem from better exploitation. We present a simple and novel variant of ensemble Q-learning called Random Ensemble Mixture (REM), which enforces optimal Bellman consistency on random convex combinations of the Q-heads of a multi-head Q-network. The batch REM agent trained offline on DQN data outperforms the batch QR-DQN and online C51 algorithms.

Connected through hyperedge HE-1 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "self supervised sim to real adaptation for visual robotic manipulation"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "challenges of real world reinforcement learning"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "off policy deep reinforcement learning without exploration"

Connected through hyperedge HE-2 (induced by source paper "interference and generalization in temporal difference learning"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards characterizing divergence in deep q learning"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "stiffness a new perspective on generalization in neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "on first order meta learning algorithms"
    - "natural environment benchmarks for reinforcement learning"
    - "generalization and regularization in dqn"

Connected through hyperedge HE-3 (induced by source paper "keep doing what worked behavioral modelling priors for offline reinforcement learning"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "off policy deep reinforcement learning without exploration"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "maximum a posteriori policy optimisation"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "a framework for data driven robotics"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "challenges of real world reinforcement learning"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-5 (induced by source paper "iris implicit reinforcement without interaction at scale for learning control from offline robot manipulation data"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "off policy policy gradient with state distribution correction"
    - "towards characterizing divergence in deep q learning"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "learning latent plans from play"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "goal conditioned imitation learning"
    - "reinforcement learning from imperfect demonstrations"

Connected through hyperedge HE-6 (induced by source paper "meta reinforcement learning from observational data"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "alphastar an evolutionary computation perspective"
    - "an introduction to variational autoencoders"
    - "meta reinforcement learning as task inference"
    - "mapping state space using landmarks for universal goal reaching"
    - "benchmarking model based reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-7 (induced by source paper "bail best action imitation learning for batch deep reinforcement learning"):
  with members:
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "off policy evaluation via off policy classification"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "reinforcement learning from imperfect demonstrations"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "off policy deep reinforcement learning without exploration"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-8 (induced by source paper "zpd teaching strategies for deep reinforcement learning from demonstrations"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "making efficient use of demonstrations to solve hard exploration problems"
    - "exploration by random network distillation"
    - "off policy deep reinforcement learning without exploration"
    - "observe and look further achieving consistent performance on atari"
    - "a practical approach to insertion with variable socket position using deep reinforcement learning"
    - "distributed prioritized experience replay"

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.LG(Machine Learning)