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: "extending detection with forensic information"
  Abstract: For over a quarter century, security-relevant detection has been driven by models learned from input features collected from real or simulated environments. An artifact (e.g., network event, potential malware sample, suspicious email) is deemed malicious or non-malicious based on its similarity to the learned model at run-time. However, the training of the models has been historically limited to only those features available at run time. In this paper, we consider an alternate model construction approach that trains models using forensic "privileged" information--features available at training time but not at runtime--to improve the accuracy and resilience of detection systems. In particular, we adapt and extend recent advances in knowledge transfer, model influence, and distillation to enable the use of forensic data in a range of security domains. Our empirical study shows that privileged information increases detection precision and recall over a system with no privileged information: we observe up to 7.7% relative decrease in detection error for fast-flux bot detection, 8.6% for malware traffic detection, 7.3% for malware classification, and 16.9% for face recognition. We explore the limitations and applications of different privileged information techniques in detection systems. Such techniques open the door to systems that can integrate forensic data directly into detection models, and therein provide a means to fully exploit the information available about past security-relevant events.

Connected through hyperedge HE-1 (induced by source paper "incorporating privileged information to unsupervised anomaly detection"):
  with members:
    - "building better detection with privileged 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-2 (induced by source paper "curie policy based secure data exchange"):
  with members:
    - "large scale distributed neural network training through online distillation"
    - "differentially private federated learning a client level perspective"
    - "achieving secure and differentially private computations in multiparty settings"
    - "a survey on homomorphic encryption schemes theory and implementation"
    - "federated multi task learning"
    - "inherit differential privacy in distributed setting multiparty randomized function computation"
    - "patient driven privacy control through generalized distillation"
    - "revisiting differentially private regression lessons from learning theory and their consequences"

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"
    - "building better detection with privileged 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"
    - "building better detection with privileged 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)