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: "query limited black box attacks to classifiers"
  Abstract: We study black-box attacks on machine learning classifiers where each query to the model incurs some cost or risk of detection to the adversary. We focus explicitly on minimizing the number of queries as a major objective. Specifically, we consider the problem of attacking machine learning classifiers subject to a budget of feature modification cost while minimizing the number of queries, where each query returns only a class and confidence score. We describe an approach that uses Bayesian optimization to minimize the number of queries, and find that the number of queries can be reduced to approximately one tenth of the number needed through a random strategy for scenarios where the feature modification cost budget is low.

Connected through hyperedge HE-1 (induced by source paper "hybrid batch attacks finding black box adversarial examples with limited queries"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "there are no bit parts for sign bits in black box attacks"
    - "simple black box adversarial attacks"
    - "stateful detection of black box adversarial attacks"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "improving black box adversarial attacks with a transfer based prior"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"

Connected through hyperedge HE-2 (induced by source paper "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "defensive quantization when efficiency meets robustness"
    - "fault sneaking attack a stealthy framework for misleading deep neural networks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "exploring the space of black box attacks on deep neural networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "graphdefense towards robust graph convolutional networks"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "adversarial defense framework for graph neural network"
    - "adversarial attacks on graph neural networks via meta learning"
    - "adversarial attack on graph structured data"
    - "graph convolutional neural networks for web scale recommender systems"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-5 (induced by source paper "black box adversarial attacks with bayesian optimization"):
  with members:
    - "procedural noise adversarial examples for black box attacks on deep convolutional networks"
    - "hopskipjumpattack a query efficient decision based attack"
    - "black box adversarial attacks with limited queries and information"
    - "query efficient hard label black box attack an optimization based approach"
    - "practical black box attacks against machine learning"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards evaluating the robustness of neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "why do deep convolutional networks generalize so poorly to small image transformations"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "studying invariances of trained convolutional neural networks"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "exploring the space of black box attacks on deep neural networks"
    - "the elephant in the room"

Connected through hyperedge HE-7 (induced by source paper "attack graph convolutional networks by adding fake nodes"):
  with members:
    - "adversarial attack on graph structured data"
    - "graph convolutional neural networks for web scale recommender systems"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "query efficient hard label black box attack an optimization based approach"
    - "towards crafting text adversarial samples"

Connected through hyperedge HE-8 (induced by source paper "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"):
  with members:
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "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"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "simple black box adversarial perturbations for deep networks"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

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.CR(Cryptography and Security)