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 "building better detection with privileged information"):
  Abstract: Modern detection systems use sensor outputs available in the deployment environment to probabilistically identify attacks. These systems are trained on past or synthetic feature vectors to create a model of anomalous or normal behavior. Thereafter, run-time collected sensor outputs are compared to the model to identify attacks (or the lack of attack). While this approach to detection has been proven to be effective in many environments, it is limited to training on only features that can be reliably collected at test-time. Hence, they fail to leverage the often vast amount of ancillary information available from past forensic analysis and post-mortem data. In short, detection systems don't train (and thus don't learn from) features that are unavailable or too costly to collect at run-time. In this paper, we leverage recent advances in machine learning to integrate privileged information --features available at training time, but not at run-time-- into detection algorithms. We apply three different approaches to model training with privileged information; knowledge transfer, model influence, and distillation, and empirically validate their performance in a range of detection domains. Our evaluation shows that privileged information can increase detector precision and recall: we observe an average of 4.8% decrease in detection error for malware traffic detection over a system with no privileged information, 3.53% for fast-flux domain bot detection, 3.33% for malware classification, 11.2% for facial user authentication. We conclude by exploring the limitations and applications of different privileged information techniques in detection systems.

Includes members:
  Member "towards evaluating the robustness of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "patient driven privacy control through generalized distillation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "curie policy based secure data exchange")
  Member "patterns for learning with side information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "incorporating privileged information to unsupervised anomaly detection")
  Member "deepid3 face recognition with very deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "novel feature extraction selection and fusion for effective malware family classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning from context exploiting and interpreting file path information for better malware detection")
  Member "distillation as a defense to adversarial perturbations against deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "privileged information for data clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hard pixel mining for depth privileged semantic segmentation")
  Member "do deep nets really need to be deep":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")

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)