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 "discor corrective feedback in reinforcement learning via distribution correction"):
  Abstract: Deep reinforcement learning can learn effective policies for a wide range of tasks, but is notoriously difficult to use due to instability and sensitivity to hyperparameters. The reasons for this remain unclear. When using standard supervised methods (e.g., for bandits), on-policy data collection provides "hard negatives" that correct the model in precisely those states and actions that the policy is likely to visit. We call this phenomenon "corrective feedback." We show that bootstrapping-based Q-learning algorithms do not necessarily benefit from this corrective feedback, and training on the experience collected by the algorithm is not sufficient to correct errors in the Q-function. In fact, Q-learning and related methods can exhibit pathological interactions between the distribution of experience collected by the agent and the policy induced by training on that experience, leading to potential instability, sub-optimal convergence, and poor results when learning from noisy, sparse or delayed rewards. We demonstrate the existence of this problem, both theoretically and empirically. We then show that a specific correction to the data distribution can mitigate this issue. Based on these observations, we propose a new algorithm, DisCor, which computes an approximation to this optimal distribution and uses it to re-weight the transitions used for training, resulting in substantial improvements in a range of challenging RL settings, such as multi-task learning and learning from noisy reward signals. Blog post presenting a summary of this work is available at: this https URL.

Includes members:
  Member "gradient surgery for multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement")
  Member "diagnosing bottlenecks in deep q learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "striving for simplicity and performance in off policy drl output normalization and non uniform sampling")
  Member "towards characterizing divergence in deep q learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interference and generalization in temporal difference learning")
  Member "behavior regularized offline reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an optimistic perspective on offline reinforcement learning")
  Member "meta world a benchmark and evaluation for multi task and meta reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning curiosity algorithms")
  Member "stabilizing off policy q learning via bootstrapping error reduction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "keep doing what worked behavioral modelling priors for offline reinforcement learning")
  Member "provably efficient q learning with function approximation via distribution shift error checking oracle":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "ray interference a source of plateaus in deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "interference and generalization in temporal difference learning")

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)