human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "samples are useful not always denoising policy gradient updates using variance explained"):
  Abstract: Policy gradient algorithms in reinforcement learning optimize the policy directly and rely on efficiently sampling an environment. However, while most sampling procedures are based solely on sampling the agent's policy, other measures directly accessible through these algorithms could be used to improve sampling before each policy update. Following this line of thoughts, we propose the use of SAUNA, a method where transitions are rejected from the gradient updates if they do not meet a particular criterion, and kept otherwise. This criterion, the fraction of variance explained $\mathcal{V}^{ex}$, is a measure of the discrepancy between a model and actual samples. In this work, $\mathcal{V}^{ex}$ is used to evaluate the impact each transition will have on learning: this criterion refines sampling and improves the policy gradient algorithm. In this paper: (a) We introduce and explore $\mathcal{V}^{ex}$, the criterion used for denoising policy gradient updates. (b) We conduct experiments across a variety of benchmark environments, including standard continuous control problems. Our results show better performance with SAUNA. (c) We investigate why $\mathcal{V}^{ex}$ provides a reliable assessment for the selection of samples that will positively impact learning. (d) We show how this criterion can work as a dynamic tool to adjust the ratio between exploration and exploitation.

Includes members:
  Member "exploration by random network distillation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "impala scalable distributed deep rl with importance weighted actor learner architectures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "quantifying generalization in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "a dissection of overfitting and generalization in continuous reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "recurrent world models facilitate policy evolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "bridging the gap between value and policy based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "proximal policy optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "sample efficient actor critic with experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

Question: Please tell me which class the source paper 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)