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: "zeroth order asynchronous doubly stochastic algorithm with variance reduction"
  Abstract: Zeroth-order (derivative-free) optimization attracts a lot of attention in machine learning, because explicit gradient calculations may be computationally expensive or infeasible. To handle large scale problems both in volume and dimension, recently asynchronous doubly stochastic zeroth-order algorithms were proposed. The convergence rate of existing asynchronous doubly stochastic zeroth order algorithms is $O(\frac{1}{\sqrt{T}})$ (also for the sequential stochastic zeroth-order optimization algorithms). In this paper, we focus on the finite sums of smooth but not necessarily convex functions, and propose an asynchronous doubly stochastic zeroth-order optimization algorithm using the accelerated technology of variance reduction (AsyDSZOVR). Rigorous theoretical analysis show that the convergence rate can be improved from $O(\frac{1}{\sqrt{T}})$ the best result of existing algorithms to $O(\frac{1}{T})$. Also our theoretical results is an improvement to the ones of the sequential stochastic zeroth-order optimization algorithms.

Connected through hyperedge HE-1 (induced by source paper "zo adamm zeroth order adaptive momentum method for black box optimization"):
  with members:
    - "on the convergence proof of amsgrad and a new version"
    - "on the convergence of adam and beyond"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-2 (induced by source paper "learning to learn by zeroth order oracle"):
  with members:
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "learning to optimize neural nets"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"
    - "guided evolutionary strategies escaping the curse of dimensionality in random search"
    - "query efficient hard label black box attack an optimization based approach"
    - "learning gradient descent better generalization and longer horizons"
    - "learned optimizers that scale and generalize"

Connected through hyperedge HE-3 (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"
    - "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"
    - "on the complexity of bandit and derivative free stochastic convex optimization"

Connected through hyperedge HE-4 (induced by source paper "accelerated method for stochastic composition optimization with nonsmooth regularization"):
  with members:
    - "fast stochastic variance reduced admm for stochastic composition optimization"
    - "learning from conditional distributions via dual kernel embeddings"

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)