human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "microsoft malware classification challenge"):
  with members:
    - "learning a neural network based representation for open set recognition"
    - "lempel ziv jaccard distance an effective alternative to ssdeep and sdhash"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "deepcloak masking deep neural network models for robustness against adversarial samples"
    - "imbalanced malware images classification a cnn based approach"
    - "itect scalable information theoretic similarity for malware detection"
    - "on the feasibility of malware authorship attribution"
    - "a theoretical framework for robustness of deep classifiers against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "incorporating privileged information to unsupervised anomaly detection"):
  with members:
    - "extending detection with forensic information"
    - "patterns for learning with side information"
    - "systematic construction of anomaly detection benchmarks from real data"
    - "learning to transfer privileged information"
    - "privileged information for data clustering"

Connected through hyperedge HE-3 (induced by source paper "achieving secure and differentially private computations in multiparty settings"):
  with members:
    - "a survey on homomorphic encryption schemes theory and implementation"
    - "extending detection with forensic information"
    - "patient driven privacy control through generalized distillation"
    - "controlled data sharing for collaborative predictive blacklisting"
    - "functional mechanism regression analysis under differential privacy"
    - "differentially private empirical risk minimization"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-4 (induced by source paper "patient driven privacy control through generalized distillation"):
  with members:
    - "feature selection a data perspective"
    - "curie policy based secure data exchange"
    - "extending detection with forensic information"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "adam a method for stochastic optimization"
    - "privacy in the genomic era"
    - "do deep nets really need to be deep"

Question: Please tell me which class the center node 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)