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: "on the complexity of bandit and derivative free stochastic convex optimization"
  Abstract: The problem of stochastic convex optimization with bandit feedback (in the learning community) or without knowledge of gradients (in the optimization community) has received much attention in recent years, in the form of algorithms and performance upper bounds. However, much less is known about the inherent complexity of these problems, and there are few lower bounds in the literature, especially for nonlinear functions. In this paper, we investigate the attainable error/regret in the bandit and derivative-free settings, as a function of the dimension d and the available number of queries T. We provide a precise characterization of the attainable performance for strongly-convex and smooth functions, which also imply a non-trivial lower bound for more general problems. Moreover, we prove that in both the bandit and derivative-free setting, the required number of queries must scale at least quadratically with the dimension. Finally, we show that on the natural class of quadratic functions, it is possible to obtain a "fast" O(1/T) error rate in terms of T, under mild assumptions, even without having access to gradients. To the best of our knowledge, this is the first such rate in a derivative-free stochastic setting, and holds despite previous results which seem to imply the contrary.

Connected through hyperedge HE-1 (induced by source paper "naive exploration is optimal for online lqr"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "learning linear dynamical systems with semi parametric least squares"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "logarithmic regret for online control"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "on optimality of adaptive linear quadratic regulators"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "spectral filtering for general linear dynamical systems"

Connected through hyperedge HE-2 (induced by source paper "logarithmic regret for learning linear quadratic regulators efficiently"):
  with members:
    - "online control with adversarial disturbances"
    - "logarithmic regret for online control"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "spectral filtering for general linear dynamical systems"
    - "online linear quadratic control"
    - "model free linear quadratic control via reduction to expert prediction"

Connected through hyperedge HE-3 (induced by source paper "bandit convex optimization in non stationary environments"):
  with members:
    - "introduction to online convex optimization"
    - "bandit convex optimization for scalable and dynamic iot management"
    - "adaptive online learning in dynamic environments"
    - "tracking the best expert in non stationary stochastic environments"
    - "dynamic regret of strongly adaptive methods"
    - "corralling a band of bandit algorithms"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"

Connected through hyperedge HE-4 (induced by source paper "online continuous submodular maximization from full information to bandit feedback"):
  with members:
    - "black box submodular maximization discrete and continuous settings"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "an optimal algorithm for bandit convex optimization"
    - "submodular functions from discrete to continous domains"
    - "online submodular maximization under a matroid constraint with application to learning assignments"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "projection free online learning"

Connected through hyperedge HE-5 (induced by source paper "memory sample tradeoffs for linear regression with small error"):
  with members:
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "detecting correlations with little memory and communication"
    - "extractor based time space lower bounds for learning"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "a concentration theorem for projections"

Connected through hyperedge HE-6 (induced by source paper "contrasting exploration in parameter and action space a zeroth order optimization perspective"):
  with members:
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "towards generalization and simplicity in continuous control"
    - "trust region policy optimization"
    - "an optimal algorithm for bandit and zero order convex optimization with two point feedback"
    - "adam a method for stochastic optimization"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-7 (induced by source paper "derivative free methods for policy optimization guarantees for linear quadratic systems"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "online linear quadratic control"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "finite time analysis of optimal adaptive policies for linear quadratic systems"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "towards generalization and simplicity in continuous control"

Connected through hyperedge HE-8 (induced by source paper "zeroth order stochastic variance reduction for nonconvex optimization"):
  with members:
    - "bandit convex optimization for scalable and dynamic iot management"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "practical black box attacks against machine learning"
    - "zeroth order asynchronous doubly stochastic algorithm with variance reduction"
    - "towards evaluating the robustness of neural networks"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "an optimal algorithm for bandit and zero order convex optimization with two point feedback"

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)