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: "g learning 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 on unlearning biased estimates of the state-action function. The bias comes 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 noise in the space of optimal actions by penalizing deterministic policies at the beginning of the learning. Moreover, it enables naturally incorporating prior distributions over optimal actions when available. The stochastic nature of G-learning also makes it more cost-effective than Q-learning in noiseless but exploration-risky domains. We illustrate these ideas in several examples where G-learning results in significant improvements of the learning rate and the learning cost.

Connected through hyperedge HE-1 (induced by source paper "information asymmetry in kl regularized rl"):
  with members:
    - "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"
    - "kickstarting deep reinforcement learning"
    - "mix match agent curricula for reinforcement learning"
    - "equivalence between policy gradients and soft q learning"
    - "deep mutual learning"
    - "trust pcl an off policy trust region method for continuous control"
    - "fixing a broken elbo"

Connected through hyperedge HE-2 (induced by source paper "robust reinforcement learning for continuous control with model misspecification"):
  with members:
    - "challenges of real world reinforcement learning"
    - "a bayesian approach to robust reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "soft actor critic algorithms and applications"
    - "unicorn continual learning with a universal off policy agent"

Connected through hyperedge HE-3 (induced by source paper "lipschitz continuity in model based reinforcement learning"):
  with members:
    - "equivalence between wasserstein and value aware model based reinforcement learning"
    - "lipschitz properties for deep convolutional networks"
    - "a distributional perspective on reinforcement learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "a unified view of entropy regularized markov decision processes"
    - "continuous deep q learning with model based acceleration"
    - "self correcting models for model based reinforcement learning"
    - "an alternative softmax operator for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "a constrained randomized shortest paths framework for optimal exploration"):
  with members:
    - "a unified view of entropy regularized markov decision processes"
    - "two betweenness centrality measures based on randomized shortest paths"
    - "an alternative softmax operator for reinforcement learning"
    - "interpolating between random walks and shortest paths a path functional approach"
    - "dynamic policy programming"

Connected through hyperedge HE-5 (induced by source paper "bridging the gap between value and policy based reinforcement learning"):
  with members:
    - "equivalence between policy gradients and soft q learning"
    - "the reactor a fast and sample efficient actor critic agent for reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "generative adversarial imitation learning"
    - "pgq combining policy gradient and q learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "an alternative softmax operator for reinforcement learning"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "planning with information processing constraints and model uncertainty in markov decision processes"):
  with members:
    - "taming the noise in reinforcement learning via soft updates"
    - "risk sensitive and robust decision making a cvar optimization approach"
    - "generalized thompson sampling for sequential decision making and causal inference"
    - "risk sensitive reinforcement learning"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "risk sensitive path integral control"
    - "a bayesian rule for adaptive control based on causal interventions"
    - "a minimum relative entropy principle for learning and acting"

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)