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: "true online temporal difference learning"
  Abstract: The temporal-difference methods TD($\lambda$) and Sarsa($\lambda$) form a core part of modern reinforcement learning. Their appeal comes from their good performance, low computational cost, and their simple interpretation, given by their forward view. Recently, new versions of these methods were introduced, called true online TD($\lambda$) and true online Sarsa($\lambda$), respectively (van Seijen & Sutton, 2014). These new versions maintain an exact equivalence with the forward view at all times, whereas the traditional versions only approximate it for small step-sizes. We hypothesize that these true online methods not only have better theoretical properties, but also dominate the regular methods empirically. In this article, we put this hypothesis to the test by performing an extensive empirical comparison. Specifically, we compare the performance of true online TD($\lambda$)/Sarsa($\lambda$) with regular TD($\lambda$)/Sarsa($\lambda$) on random MRPs, a real-world myoelectric prosthetic arm, and a domain from the Arcade Learning Environment. We use linear function approximation with tabular, binary, and non-binary features. Our results suggest that the true online methods indeed dominate the regular methods. Across all domains/representations the learning speed of the true online methods are often better, but never worse than that of the regular methods. An additional advantage is that no choice between traces has to be made for the true online methods. Besides the empirical results, we provide an in-depth analysis of the theory behind true online temporal-difference learning. In addition, we show that new true online temporal-difference methods can be derived by making changes to the online forward view and then rewriting the update equations.

Connected through hyperedge HE-1 (induced by source paper "meta learning state based eligibility traces for more sample efficient policy evaluation"):
  with members:
    - "directly estimating the variance of the lambda return using temporal difference methods"
    - "adaptive lambda least squares temporal difference learning"
    - "a greedy approach to adapting the trace parameter for temporal difference learning"

Connected through hyperedge HE-2 (induced by source paper "market making via reinforcement learning"):
  with members:
    - "multi step reinforcement learning a unifying algorithm"
    - "a distributional perspective on reinforcement learning"
    - "the option critic architecture"
    - "reinforcement learning with parameterized actions"

Connected through hyperedge HE-3 (induced by source paper "a unified approach for multi step temporal difference learning with eligibility traces in reinforcement learning"):
  with members:
    - "multi step reinforcement learning a unifying algorithm"

Connected through hyperedge HE-4 (induced by source paper "tidbd adapting temporal difference step sizes through stochastic meta descent"):
  with members:
    - "temporal difference updating without a learning rate"

Connected through hyperedge HE-5 (induced by source paper "towards a common implementation of reinforcement learning for multiple robotic tasks"):
  with members:
    - "deep exploration via bootstrapped dqn"
    - "asynchronous methods for deep reinforcement learning"
    - "prioritized experience replay"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "continuous control with deep reinforcement learning"
    - "incentivizing exploration in reinforcement learning with deep predictive models"
    - "towards vision based deep reinforcement learning for robotic motion control"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "accelerated gradient temporal difference learning"):
  with members:
    - "investigating practical linear temporal difference learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "incremental truncated lstd"
    - "res regularized stochastic bfgs algorithm"
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"

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.AI(Artificial Intelligence)