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: "practical kernel based reinforcement learning"
  Abstract: Kernel-based reinforcement learning (KBRL) stands out among reinforcement learning algorithms for its strong theoretical guarantees. By casting the learning problem as a local kernel approximation, KBRL provides a way of computing a decision policy which is statistically consistent and converges to a unique solution. Unfortunately, the model constructed by KBRL grows with the number of sample transitions, resulting in a computational cost that precludes its application to large-scale or on-line domains. In this paper we introduce an algorithm that turns KBRL into a practical reinforcement learning tool. Kernel-based stochastic factorization (KBSF) builds on a simple idea: when a transition matrix is represented as the product of two stochastic matrices, one can swap the factors of the multiplication to obtain another transition matrix, potentially much smaller, which retains some fundamental properties of its precursor. KBSF exploits such an insight to compress the information contained in KBRL's model into an approximator of fixed size. This makes it possible to build an approximation that takes into account both the difficulty of the problem and the associated computational cost. KBSF's computational complexity is linear in the number of sample transitions, which is the best one can do without discarding data. Moreover, the algorithm's simple mechanics allow for a fully incremental implementation that makes the amount of memory used independent of the number of sample transitions. The result is a kernel-based reinforcement learning algorithm that can be applied to large-scale problems in both off-line and on-line regimes. We derive upper bounds for the distance between the value functions computed by KBRL and KBSF using the same data. We also illustrate the potential of our algorithm in an extensive empirical study in which KBSF is applied to difficult tasks based on real-world data.

Connected through hyperedge HE-1 (induced by source paper "q learning for mean field controls"):
  with members:
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "learning to control in metric space with optimal regret"
    - "natural actor critic converges globally for hierarchical linear quadratic regulator"
    - "model based reinforcement learning for atari"
    - "an algorithmic perspective on imitation learning"
    - "q learning with nearest neighbors"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "efficient reinforcement learning in deterministic systems with value function generalization"

Connected through hyperedge HE-2 (induced by source paper "barrier certified adaptive reinforcement learning with applications to brushbot navigation"):
  with members:
    - "safe learning of quadrotor dynamics using barrier certificates"
    - "constrained policy optimization"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "safe policy search for lifelong reinforcement learning with sublinear regret"
    - "adaptive learning in cartesian product of reproducing kernel hilbert spaces"
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"

Connected through hyperedge HE-3 (induced by source paper "rl4health crowdsourcing reinforcement learning for knee replacement pathway optimization"):
  with members:
    - "improving sepsis treatment strategies by combining deep and kernel based reinforcement learning"
    - "state aggregation learning from markov transition data"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "continuous state space models for optimal sepsis treatment a deep reinforcement learning approach"
    - "deep reinforcement learning an overview"
    - "a reinforcement learning approach to weaning of mechanical ventilation in intensive care units"
    - "deep reinforcement learning for sepsis treatment"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "q learning with nearest neighbors"):
  with members:
    - "finite sample analyses for td 0 with function approximation"
    - "benchmarking deep reinforcement learning for continuous control"
    - "the boundary forest algorithm for online supervised and unsupervised learning"

Connected through hyperedge HE-5 (induced by source paper "target driven visual navigation in indoor scenes using deep reinforcement learning"):
  with members:
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "asynchronous methods for deep reinforcement learning"
    - "fast robust continuous monocular egomotion computation"
    - "building machines that learn and think like people"
    - "learning physical intuition of block towers by example"
    - "what happens if learning to predict the effect of forces in images"
    - "state of the art control of atari games using shallow reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "a self taught artificial agent for multi physics computational model personalization"):
  with members:
    - "massively parallel methods for deep reinforcement learning"
    - "reinforcement learning a survey"

Connected through hyperedge HE-7 (induced by source paper "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"):
  with members:
    - "galerkin methods for complementarity problems and variational inequalities"
    - "a dantzig selector approach to temporal difference learning"
    - "control design for markov chains under safety constraints a convex approach"
    - "representation policy iteration"
    - "sparse online learning via truncated gradient"
    - "risk sensitive reinforcement learning applied to control under constraints"

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)