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 "reconciling lambda returns with experience replay"):
  Abstract: Modern deep reinforcement learning methods have departed from the incremental learning required for eligibility traces, rendering the implementation of the $\lambda$-return difficult in this context. In particular, off-policy methods that utilize experience replay remain problematic because their random sampling of minibatches is not conducive to the efficient calculation of $\lambda$-returns. Yet replay-based methods are often the most sample efficient, and incorporating $\lambda$-returns into them is a viable way to achieve new state-of-the-art performance. Towards this, we propose the first method to enable practical use of $\lambda$-returns in arbitrary replay-based methods without relying on other forms of decorrelation such as asynchronous gradient updates. By promoting short sequences of past transitions into a small cache within the replay memory, adjacent $\lambda$-returns can be efficiently precomputed by sharing Q-values. Computation is not wasted on experiences that are never sampled, and stored $\lambda$-returns behave as stable temporal-difference (TD) targets that replace the target network. Additionally, our method grants the unique ability to observe TD errors prior to sampling; for the first time, transitions can be prioritized by their true significance rather than by a proxy to it. Furthermore, we propose the novel use of the TD error to dynamically select $\lambda$-values that facilitate faster learning. We show that these innovations can enhance the performance of DQN when playing Atari 2600 games, even under partial observability. While our work specifically focuses on $\lambda$-returns, these ideas are applicable to any multi-step return estimator.

Includes members:
  Member "discrete sequential prediction of continuous actions for deep rl":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "particle based adaptive discretization for continuous control using deep reinforcement learning")
  Member "investigating recurrence and eligibility traces in deep q networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient eligibility traces for deep reinforcement learning")
  Member "learning complex dexterous manipulation with deep reinforcement learning and demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchically decoupled imitation for morphological transfer")
  Member "emergence of locomotion behaviours in rich environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "learning hand eye coordination for robotic grasping with deep learning and large scale data collection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "continuous deep q learning with model based acceleration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "safe and efficient off policy reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interference and generalization in temporal difference learning")
  Member "benchmarking deep reinforcement learning for continuous control":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")

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)