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: "safe and efficient off policy reinforcement learning"
  Abstract: In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing these in a common form, we derive a novel algorithm, Retrace($\lambda$), with three desired properties: (1) it has low variance; (2) it safely uses samples collected from any behaviour policy, whatever its degree of "off-policyness"; and (3) it is efficient as it makes the best use of samples collected from near on-policy behaviour policies. We analyze the contractive nature of the related operator under both off-policy policy evaluation and control settings and derive online sample-based algorithms. We believe this is the first return-based off-policy control algorithm converging a.s. to $Q^*$ without the GLIE assumption (Greedy in the Limit with Infinite Exploration). As a corollary, we prove the convergence of Watkins' Q($\lambda$), which was an open problem since 1989. We illustrate the benefits of Retrace($\lambda$) on a standard suite of Atari 2600 games.

Connected through hyperedge HE-1 (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"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "on first order meta learning algorithms"
    - "natural environment benchmarks for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "never give up learning directed exploration strategies"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "mulex disentangling exploitation from exploration in deep rl"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "meta gradient reinforcement learning"
    - "an introduction to deep reinforcement learning"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"

Connected through hyperedge HE-3 (induced by source paper "universal value density estimation for imitation learning and goal conditioned reinforcement learning"):
  with members:
    - "sqil imitation learning via reinforcement learning with sparse rewards"
    - "random expert distillation imitation learning via expert policy support estimation"
    - "generative predecessor models for sample efficient imitation learning"
    - "is the policy gradient a gradient"
    - "sqil imitation learning via regularized behavioral cloning"
    - "visual hindsight experience replay"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-4 (induced by source paper "taylor expansion policy optimization"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "adaptive trade offs in off policy learning"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "policy optimization through approximated importance sampling"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "observe and look further achieving consistent performance on atari"
    - "distributed prioritized experience replay"
    - "policy optimization via importance sampling"

Connected through hyperedge HE-5 (induced by source paper "policy evaluation networks"):
  with members:
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "the value function polytope in reinforcement learning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "value prediction network"
    - "a distributional perspective on reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "can increasing input dimensionality improve deep reinforcement learning"):
  with members:
    - "agent modeling as auxiliary task for deep reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "decoupling dynamics and reward for transfer learning"
    - "sim to real transfer learning using robustified controllers in robotic tasks involving complex dynamics"
    - "self normalizing neural networks"
    - "autoencoder augmented neuroevolution for visual doom playing"

Connected through hyperedge HE-7 (induced by source paper "safe counterfactual reinforcement learning"):
  with members:
    - "intrinsically efficient stable and bounded off policy evaluation for reinforcement learning"
    - "off policy evaluation via off policy classification"
    - "importance sampling policy evaluation with an estimated behavior policy"
    - "efficient counterfactual learning from bandit feedback"
    - "representation balancing mdps for off policy policy evaluation"
    - "more robust doubly robust off policy evaluation"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"
    - "data efficient off policy policy evaluation for reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "adaptive estimator selection for off policy evaluation"):
  with members:
    - "doubly robust off policy evaluation with shrinkage"
    - "empirical study of off policy policy evaluation for reinforcement learning"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "more robust doubly robust off policy evaluation"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "off policy evaluation for slate recommendation"
    - "doubly robust off policy value evaluation for 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.LG(Machine Learning)