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 "a case study on using deep learning for network intrusion detection"):
  Abstract: Deep Learning has been very successful in many application domains. However, its usefulness in the context of network intrusion detection has not been systematically investigated. In this paper, we report a case study on using deep learning for both supervised network intrusion detection and unsupervised network anomaly detection. We show that Deep Neural Networks (DNNs) can outperform other machine learning based intrusion detection systems, while being robust in the presence of dynamic IP addresses. We also show that Autoencoders can be effective for network anomaly detection.

Includes members:
  Member "metrics towards measuring cyber agility":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on ethereum systems security vulnerabilities attacks and defenses")
  Member "vuldeepecker a deep learning based system for vulnerability detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "a novel hierarchical intrusion detection system based on decision tree and rules based models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an efficient network intrusion detection system based on feature selection and ensemble classifier")
  Member "characterizing honeypot captured cyber attacks statistical framework and case study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "metrics towards measuring cyber agility")
  Member "entity embeddings of categorical variables":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fastai a layered api for deep learning")
  Member "predicting cyber attack rates with extreme values":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "metrics towards measuring cyber agility")
  Member "preventive and reactive cyber defense dynamics is globally stable":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "preventive and reactive cyber defense dynamics with ergodic time dependent parameters is globally attractive")
  Member "spatiotemporal patterns and predictability of cyberattacks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "metrics towards measuring cyber agility")

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)