human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "optimization guided binary diversification to mislead neural networks for malware detection"):
  Abstract: Motivated by the transformative impact of deep neural networks (DNNs) on different areas (e.g., image and speech recognition), researchers and anti-virus vendors are proposing end-to-end DNNs for malware detection from raw bytes that do not require manual feature engineering. Given the security sensitivity of the task that these DNNs aim to solve, it is important to assess their susceptibility to evasion. #R##N#In this work, we propose an attack that guides binary-diversification tools via optimization to mislead DNNs for malware detection while preserving the functionality of binaries. Unlike previous attacks on such DNNs, ours manipulates instructions that are a functional part of the binary, which makes it particularly challenging to defend against. We evaluated our attack against three DNNs in white-box and black-box settings, and found that it can often achieve success rates near 100%. Moreover, we found that our attack can fool some commercial anti-viruses, in certain cases with a success rate of 85%. We explored several defenses, both new and old, and identified some that can successfully prevent over 80% of our evasion attempts. However, these defenses may still be susceptible to evasion by adaptive attackers, and so we advocate for augmenting malware-detection systems with methods that do not rely on machine learning.

Includes members:
  Member "certified adversarial robustness via randomized smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "misleading authorship attribution of source code using adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "backdooring and poisoning neural networks with image scaling attacks")
  Member "explaining vulnerabilities of deep learning to adversarial malware binaries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "theoretically principled trade off between robustness and accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "counterstrike defending deep learning architectures against adversarial samples by langevin dynamics with supervised denoising autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "n ml mitigating adversarial examples via ensembles of topologically manipulated classifiers")
  Member "ember an open dataset for training static pe malware machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "feature denoising for improving adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "on the suitability of l_p norms for creating and preventing adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images")

Question: Please tell me which class the source paper 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)