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: "black box attacks against rnn based malware detection algorithms"
  Abstract: Recent researches have shown that machine learning based malware detection algorithms are very vulnerable under the attacks of adversarial examples. These works mainly focused on the detection algorithms which use features with fixed dimension, while some researchers have begun to use recurrent neural networks (RNN) to detect malware based on sequential API features. This paper proposes a novel algorithm to generate sequential adversarial examples, which are used to attack a RNN based malware detection system. It is usually hard for malicious attackers to know the exact structures and weights of the victim RNN. A substitute RNN is trained to approximate the victim RNN. Then we propose a generative RNN to output sequential adversarial examples from the original sequential malware inputs. Experimental results showed that RNN based malware detection algorithms fail to detect most of the generated malicious adversarial examples, which means the proposed model is able to effectively bypass the detection algorithms.

Connected through hyperedge HE-1 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers"):
  with members:
    - "understanding the efficacy reliability and resiliency of computer vision techniques for malware detection and future research directions"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "on defending against label flipping attacks on malware detection systems"
    - "adversarial samples on android malware detection systems for iot systems"
    - "copycat practical adversarial attacks on visualization based malware detection"
    - "generation evaluation of adversarial examples for malware obfuscation"
    - "examining adversarial learning against graph based iot malware detection systems"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"

Connected through hyperedge HE-2 (induced by source paper "advjnd generating adversarial examples with just noticeable difference"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "perceptual evaluation of adversarial attacks for cnn based image classification"
    - "a general framework for adversarial examples with objectives"
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "densely connected convolutional networks"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-3 (induced by source paper "a4 evading learning based adblockers"):
  with members:
    - "filter list generation for underserved regions"
    - "percival making in browser perceptual ad blocking practical with deep learning"
    - "adgraph a graph based approach to ad and tracker blocking"
    - "the future of ad blocking an analytical framework and new techniques"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "poba ga perturbation optimized black box adversarial attacks via genetic algorithm"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "identify susceptible locations in medical records via adversarial attacks on deep predictive models"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "security analysis and enhancement of model compressed deep learning systems under adversarial attacks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "asp a fast adversarial attack example generation framework based on adversarial saliency prediction"

Connected through hyperedge HE-5 (induced by source paper "defense methods against adversarial examples for recurrent neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating natural language adversarial examples"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "robust neural malware detection models for emulation sequence learning"

Connected through hyperedge HE-6 (induced by source paper "effectiveness of adversarial examples and defenses for malware classification"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "attack and defense of dynamic analysis based adversarial neural malware classification models"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "hardening malware detection systems against cyber maneuvers an adversarial machine learning approach"
    - "improved training of wasserstein gans"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "generic black box end to end attack against state of the art api call based malware classifiers"

Connected through hyperedge HE-7 (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-8 (induced by source paper "generation evaluation of adversarial examples for malware obfuscation"):
  with members:
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "deceiving end to end deep learning malware detectors using adversarial examples"
    - "microsoft malware classification challenge"
    - "imbalanced malware images classification a cnn based approach"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "xgboost a scalable tree boosting system"
    - "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)