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 "text a 3 activation anomaly analysis"):
  Abstract: Inspired by the recent advances in coverage-guided analysis of neural networks (NNs), we propose a novel anomaly detection approach. We show that the hidden activation values in NNs contain information to distinguish between normal and anomalous samples. Common approaches for anomaly detection base the amount of novelty of each data point solely on one single decision variable. We refine this approach by incorporating the entire context of the model. With our data-driven method, we achieve strong anomaly detection results on common baseline data sets, e.g., MNIST and CSE-CIC-IDS2018, purely based on the automatic analysis of the data. Our anomaly detection method allows to easily inspect data across different domains for anomalies without expert knowledge.

Includes members:
  Member "dla dense layer analysis for adversarial example detection":
    also connected through other hyperedges:
      (none)
  Member "deep learning for anomaly detection a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "kitsune an ensemble of autoencoders for online network intrusion detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparseids learning packet sampling with reinforcement learning")
  Member "deepgauge multi granularity testing criteria for deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "importance driven deep learning system testing")
  Member "testing deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "importance driven deep learning system testing")
  Member "deepxplore automated whitebox testing of deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "importance driven deep learning system testing")
  Member "lstm based encoder decoder for multi sensor anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structural deep clustering network")

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)