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 stochastic variance reduction for nonconvex optimization"
  Abstract: As application demands for zeroth-order (gradient-free) optimization accelerate, the need for variance reduced and faster converging approaches is also intensifying. This paper addresses these challenges by presenting: a) a comprehensive theoretical analysis of variance reduced zeroth-order (ZO) optimization, b) a novel variance reduced ZO algorithm, called ZO-SVRG, and c) an experimental evaluation of our approach in the context of two compelling applications, black-box chemical material classification and generation of adversarial examples from black-box deep neural network models. Our theoretical analysis uncovers an essential difficulty in the analysis of ZO-SVRG: the unbiased assumption on gradient estimates no longer holds. We prove that compared to its first-order counterpart, ZO-SVRG with a two-point random gradient estimator could suffer an additional error of order $O(1/b)$, where $b$ is the mini-batch size. To mitigate this error, we propose two accelerated versions of ZO-SVRG utilizing variance reduced gradient estimators, which achieve the best rate known for ZO stochastic optimization (in terms of iterations). Our extensive experimental results show that our approaches outperform other state-of-the-art ZO algorithms, and strike a balance between the convergence rate and the function query complexity.

Connected through hyperedge HE-1 (induced by source paper "towards an efficient and general framework of robust training for graph neural networks"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "zo adamm zeroth order adaptive momentum method for black box optimization"
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adversarial attack on graph structured data"
    - "universal decision based black box perturbations breaking security through obscurity defenses"

Connected through hyperedge HE-2 (induced by source paper "man in the middle attacks against machine learning classifiers via malicious generative models"):
  with members:
    - "diagnosing and enhancing vae models"
    - "semantic adversarial examples"
    - "model reuse attacks on deep learning systems"
    - "genattack practical black box attacks with gradient free optimization"
    - "generating adversarial examples with adversarial networks"
    - "black box adversarial attacks with limited queries and information"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-3 (induced by source paper "hopskipjumpattack a query efficient decision based attack"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "black box adversarial attacks with limited queries and information"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "mitigating adversarial effects through randomization"
    - "mitigating evasion attacks to deep neural networks via region based classification"

Connected through hyperedge HE-4 (induced by source paper "adaptive weight decay for deep neural networks"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "don t decay the learning rate increase the batch size"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "sharp minima can generalize for deep nets"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-5 (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"
    - "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"
    - "query efficient hard label black box attack an optimization based approach"

Connected through hyperedge HE-6 (induced by source paper "model agnostic contrastive explanations for structured data"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "improving simple models with confidence profiles"
    - "interpreting neural network judgments via minimal stable and symbolic corrections"
    - "methods for interpreting and understanding deep neural networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "tip typifying the interpretability of procedures"
    - "visual explanation by interpretation improving visual feedback capabilities of deep neural networks"
    - "interpreting blackbox models via model extraction"

Connected through hyperedge HE-7 (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"
    - "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"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-8 (induced by source paper "sign opt a query efficient hard label adversarial attack"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "there are no bit parts for sign bits in black box attacks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "black box adversarial attacks with limited queries and information"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "practical black box attacks against machine learning"
    - "towards evaluating the robustness of neural networks"

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)