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: "statistical linear estimation with penalized estimators an application to reinforcement learning"
  Abstract: Motivated by value function estimation in reinforcement learning, we study statistical linear inverse problems, i.e., problems where the coefficients of a linear system to be solved are observed in noise. We consider penalized estimators, where performance is evaluated using a matrix-weighted two-norm of the defect of the estimator measured with respect to the true, unknown coefficients. Two objective functions are considered depending whether the error of the defect measured with respect to the noisy coefficients is squared or unsquared. We propose simple, yet novel and theoretically well-founded data-dependent choices for the regularization parameters for both cases that avoid data-splitting. A distinguishing feature of our analysis is that we derive deterministic error bounds in terms of the error of the coefficients, thus allowing the complete separation of the analysis of the stochastic properties of these errors. We show that our results lead to new insights and bounds for linear value function estimation in reinforcement learning.

Connected through hyperedge HE-1 (induced by source paper "information theoretic considerations in batch reinforcement learning"):
  with members:
    - "a theoretical analysis of deep q learning"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "on oracle efficient pac rl with rich observations"
    - "deep reinforcement learning and the deadly triad"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "scalable bilinear pi learning using state and action features"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "near optimal behavior via approximate state abstraction"

Connected through hyperedge HE-2 (induced by source paper "regularized learning for domain adaptation under label shifts"):
  with members:
    - "detecting and correcting for label shift with black box predictors"
    - "active learning for cost sensitive classification"
    - "reinforcement learning of pomdp s using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"
    - "mixture proportion estimation via kernel embedding of distributions"
    - "deep residual learning for image recognition"
    - "a method of moments for mixture models and hidden markov models"
    - "on causal and anticausal learning"

Connected through hyperedge HE-3 (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"
    - "sparse q learning with mirror descent"
    - "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-4 (induced by source paper "fast lstd using stochastic approximation finite time analysis and application to traffic control"):
  with members:
    - "on td 0 with function approximation concentration bounds and a centered variant with exponential convergence"
    - "fast gradient descent for drifting least squares regression with application to bandits"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "a contextual bandit approach to personalized news article recommendation"

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)