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: "sparse q learning with mirror descent"
  Abstract: This paper explores a new framework for reinforcement learning based on online convex optimization, in particular mirror descent and related algorithms. Mirror descent can be viewed as an enhanced gradient method, particularly suited to minimization of convex functions in highdimensional spaces. Unlike traditional gradient methods, mirror descent undertakes gradient updates of weights in both the dual space and primal space, which are linked together using a Legendre transform. Mirror descent can be viewed as a proximal algorithm where the distance generating function used is a Bregman divergence. A new class of proximal-gradient based temporal-difference (TD) methods are presented based on different Bregman divergences, which are more powerful than regular TD learning. Examples of Bregman divergences that are studied include p-norm functions, and Mahalanobis distance based on the covariance of sample gradients. A new family of sparse mirror-descent reinforcement learning methods are proposed, which are able to find sparse fixed points of an l1-regularized Bellman equation at significantly less computational cost than previous methods based on second-order matrix methods. An experimental study of mirror-descent reinforcement learning is presented using discrete and continuous Markov decision processes.

Connected through hyperedge HE-1 (induced by source paper "imitation projected programmatic reinforcement learning"):
  with members:
    - "batch policy learning under constraints"
    - "control regularization for reduced variance reinforcement learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "houdini lifelong learning as program synthesis"
    - "safe exploration in continuous action spaces"
    - "predictor corrector policy optimization"
    - "programmatically interpretable reinforcement learning"
    - "accelerating imitation learning with predictive models"

Connected through hyperedge HE-2 (induced by source paper "robust blind deconvolution via mirror descent"):
  with members:
    - "deep generative filter for motion deblurring"
    - "motion deblurring in the wild"
    - "a neural approach to blind motion deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "train faster generalize better stability of stochastic gradient descent"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "understanding alternating minimization for matrix completion"

Connected through hyperedge HE-3 (induced by source paper "mirror descent search and its acceleration"):
  with members:
    - "proximal policy optimization algorithms"
    - "guided policy search as approximate mirror descent"
    - "provable bayesian inference via particle mirror descent"
    - "trust region policy optimization"
    - "path integral policy improvement with covariance matrix adaptation"

Connected through hyperedge HE-4 (induced by source paper "dantzig selector with an approximately optimal denoising matrix and its application to reinforcement learning"):
  with members:
    - "a dantzig selector approach to temporal difference learning"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-5 (induced by source paper "on convergence of some gradient based temporal differences algorithms for off policy learning"):
  with members:
    - "multi step off policy learning without importance sampling ratios"
    - "weak convergence properties of constrained emphatic temporal difference learning with constant and slowly diminishing stepsize"
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"

Connected through hyperedge HE-6 (induced by source paper "weak convergence properties of constrained emphatic temporal difference learning with constant and slowly diminishing stepsize"):
  with members:
    - "some simulation results for emphatic temporal difference learning algorithms"
    - "emphatic temporal difference learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"
    - "off policy learning with eligibility traces a survey"
    - "statistical linear estimation with penalized estimators an application to reinforcement learning"
    - "should one compute the temporal difference fix point or minimize the bellman residual the unified oblique projection view"
    - "preconditioned temporal difference learning"

Connected through hyperedge HE-7 (induced by source paper "an analysis of state relevance weights and sampling distributions on l1 regularized approximate linear programming approximation accuracy"):
  with members:
    - "value function approximation in noisy environments using locally smoothed regularized approximate linear programs"
    - "feature selection using regularization in approximate linear programs for markov decision processes"

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)