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: "taming the noise in reinforcement learning via soft updates"
  Abstract: Model-free reinforcement learning algorithms, such as Q-learning, perform poorly in the early stages of learning in noisy environments, because much effort is spent unlearning biased estimates of the state-action value function. The bias results from selecting, among several noisy estimates, the apparent optimum, which may actually be suboptimal. We propose G-learning, a new off-policy learning algorithm that regularizes the value estimates by penalizing deterministic policies in the beginning of the learning process. We show that this method reduces the bias of the value-function estimation, leading to faster convergence to the optimal value and the optimal policy. Moreover, G-learning enables the natural incorporation of prior domain knowledge, when available. The stochastic nature of G-learning also makes it avoid some exploration costs, a property usually attributed only to on-policy algorithms. We illustrate these ideas in several examples, where G-learning results in significant improvements of the convergence rate and the cost of the learning process.

Connected through hyperedge HE-1 (induced by source paper "addressing value estimation errors in reinforcement learning with a state action return distribution function"):
  with members:
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "distributed prioritized experience replay"
    - "equivalence between policy gradients and soft q learning"
    - "a distributional perspective on reinforcement learning"
    - "distributional reinforcement learning with quantile regression"
    - "emergence of locomotion behaviours in rich environments"
    - "bridging the gap between value and policy based reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "way off policy batch deep reinforcement learning of implicit human preferences in dialog"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "off policy policy gradient with state distribution correction"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "off policy deep reinforcement learning without exploration"
    - "the design and implementation of xiaoice an empathetic social chatbot"

Connected through hyperedge HE-3 (induced by source paper "exploiting hierarchy for learning and transfer in kl regularized rl"):
  with members:
    - "information asymmetry in kl regularized rl"
    - "learning to share and hide intentions using information regularization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-4 (induced by source paper "algaedice policy gradient from arbitrary experience"):
  with members:
    - "minimax weight and q function learning for off policy evaluation"
    - "off policy policy gradient with state distribution correction"
    - "doubly robust bias reduction in infinite horizon off policy estimation"
    - "imitation learning via off policy distribution matching"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "a theory of regularized markov decision processes"
    - "a kernel loss for solving the bellman equation"

Connected through hyperedge HE-5 (induced by source paper "a unified bellman optimality principle combining reward maximization and empowerment"):
  with members:
    - "mutual information regularization in markov decision processes and actor critic learning"
    - "empowerment driven exploration using mutual information estimation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "a unified strategy for implementing curiosity and empowerment driven reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-6 (induced by source paper "information theoretic model predictive q learning"):
  with members:
    - "an online learning approach to model predictive control"
    - "deepmind control suite"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "model ensemble trust region policy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "model based active exploration"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-7 (induced by source paper "policy optimization with stochastic mirror descent"):
  with members:
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "trajectory wise control variates for variance reduction in policy gradient methods"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "addressing function approximation error in actor critic methods"
    - "predictor corrector policy optimization"
    - "stochastic variance reduced policy gradient"

Connected through hyperedge HE-8 (induced by source paper "meta adaptation using importance weighted demonstrations"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "an algorithmic perspective on imitation learning"
    - "shared multi task imitation learning for indoor self navigation"
    - "learning to learn how to learn self adaptive visual navigation using meta learning"
    - "hierarchical imitation and reinforcement learning"
    - "learning to reweight examples for robust deep learning"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "recurrent world models facilitate policy evolution"

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)