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 "raid randomized adversarial input detection for neural networks"):
  Abstract: In recent years, neural networks have become the default choice for image classification and many other learning tasks, even though they are vulnerable to so-called adversarial attacks. To increase their robustness against these attacks, there have emerged numerous detection mechanisms that aim to automatically determine if an input is adversarial. However, state-of-the-art detection mechanisms either rely on being tuned for each type of attack, or they do not generalize across different attack types. To alleviate these issues, we propose a novel technique for adversarial-image detection, RAID, that trains a secondary classifier to identify differences in neuron activation values between benign and adversarial inputs. Our technique is both more reliable and more effective than the state of the art when evaluated against six popular attacks. Moreover, a straightforward extension of RAID increases its robustness against detection-aware adversaries without affecting its effectiveness.

Includes members:
  Member "when explainability meets adversarial learning detecting adversarial examples using shap signatures":
    also connected through other hyperedges:
      (none)
  Member "are odds really odd bypassing statistical detection of adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "detecting and diagnosing adversarial images with class conditional capsule reconstructions")
  Member "deepsmartfuzzer reward guided test generation for deep learning":
    also connected through other hyperedges:
      (none)
  Member "deepfault fault localization for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "importance driven deep learning system testing")
  Member "a new defense against adversarial images turning a weakness into a strength":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on adaptive attacks to adversarial example defenses")
  Member "the odds are odd a statistical test for detecting adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on adaptive attacks to adversarial example defenses")
  Member "on evaluating adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "deepgauge multi granularity testing criteria for deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "importance driven deep learning system testing")

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.LG(Machine Learning)