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 "microsoft malware classification challenge"):
  Abstract: The Microsoft Malware Classification Challenge was announced in 2015 along with a publication of a huge dataset of nearly 0.5 terabytes, consisting of disassembly and bytecode of more than 20K malware samples. Apart from serving in the Kaggle competition, the dataset has become a standard benchmark for research on modeling malware behaviour. To date, the dataset has been cited in more than 50 research papers. Here we provide a high-level comparison of the publications citing the dataset. The comparison simplifies finding potential research directions in this field and future performance evaluation of the dataset.

Includes members:
  Member "learning a neural network based representation for open set recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recent advances in open set recognition a survey")
  Member "lempel ziv jaccard distance an effective alternative to ssdeep and sdhash":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a new burrows wheeler transform markov distance")
  Member "feature squeezing detecting adversarial examples in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deepcloak masking deep neural network models for robustness against adversarial samples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "imbalanced malware images classification a cnn based approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generation evaluation of adversarial examples for malware obfuscation")
  Member "itect scalable information theoretic similarity for malware detection":
    also connected through other hyperedges:
      (none)
  Member "on the feasibility of malware authorship attribution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "end to end deep neural networks and transfer learning for automatic analysis of nation state malware")
  Member "building better detection with privileged information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "incorporating privileged information to unsupervised anomaly detection")

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)