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: "an optimal algorithm for bandit and zero order convex optimization with two point feedback"
  Abstract: We consider the closely related problems of bandit convex optimization with two-point feedback, and zero-order stochastic convex optimization with two function evaluations per round. We provide a simple algorithm and analysis which is optimal for convex Lipschitz functions. This improves on \cite{dujww13}, which only provides an optimal result for smooth functions; Moreover, the algorithm and analysis are simpler, and readily extend to non-Euclidean problems. The algorithm is based on a small but surprisingly powerful modification of the gradient estimator.

Connected through hyperedge HE-1 (induced by source paper "compositional adam an adaptive compositional solver"):
  with members:
    - "online meta learning"
    - "a survey of optimization methods from a machine learning perspective"
    - "on the convergence of adam and beyond"
    - "derivative free order robust optimisation"
    - "toward multimodal model agnostic meta learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"
    - "learning to communicate implicitly by actions"

Connected through hyperedge HE-2 (induced by source paper "black box submodular maximization discrete and continuous settings"):
  with members:
    - "introduction to online convex optimization"
    - "online continuous submodular maximization from full information to bandit feedback"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "black box adversarial attacks with limited queries and information"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"
    - "continuous dr submodular maximization structure and algorithms"

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 "min max optimization without gradients convergence and applications to adversarial ml"):
  with members:
    - "beyond adversarial training min max optimization in adversarial attack and defense"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "robust optimization over multiple domains"
    - "spectral signatures in backdoor attacks"
    - "caad 2018 iterative ensemble adversarial attack"
    - "approximating nash equilibria for black box games a bayesian optimization approach"
    - "towards distributed coevolutionary gans"

Connected through hyperedge HE-5 (induced by source paper "flo fast and lightweight hyperparameter optimization for automl"):
  with members:
    - "an open source automl benchmark"
    - "autotune a derivative free optimization framework for hyperparameter tuning"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "xgboost a scalable tree boosting system"
    - "automating biomedical data science through tree based pipeline optimization"
    - "non stochastic best arm identification and hyperparameter optimization"

Connected through hyperedge HE-6 (induced by source paper "bandit convex optimization for scalable and dynamic iot management"):
  with members:
    - "a survey on mobile edge computing the communication perspective"
    - "an online secretary framework for fog network formation with minimal latency"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "online learning for wireless distributed computing"
    - "online optimization competing with dynamic comparators"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"
    - "efficient multi user computation offloading for mobile edge cloud computing"

Connected through hyperedge HE-7 (induced by source paper "gradientless descent high dimensional zeroth order optimization"):
  with members:
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "hyperparameter optimization a spectral approach"
    - "proximal policy optimization algorithms"
    - "practical black box attacks against machine learning"
    - "adaptive bound optimization for online convex optimization"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-8 (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"
    - "adam a method for stochastic optimization"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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)