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: "controlled data sharing for collaborative predictive blacklisting"
  Abstract: Although sharing data across organizations is often advocated as a promising way to enhance cybersecurity, collaborative initiatives are rarely put into practice owing to confidentiality, trust, and liability challenges. In this paper, we investigate whether collaborative threat mitigation can be realized via a controlled data sharing approach, whereby organizations make informed decisions as to whether or not, and how much, to share. Using appropriate cryptographic tools, entities can estimate the benefits of collaboration and agree on what to share in a privacy-preserving way, without having to disclose their datasets. We focus on collaborative predictive blacklisting, i.e., forecasting attack sources based on one's logs and those contributed by other organizations. We study the impact of different sharing strategies by experimenting on a real-world dataset of two billion suspicious IP addresses collected from Dshield over two months. We find that controlled data sharing yields up to 105% accuracy improvement on average, while also reducing the false positive rate.

Connected through hyperedge HE-1 (induced by source paper "on collaborative predictive blacklisting"):
  with members:
    - "efficient private statistics with succinct sketches"
    - "predictive blacklisting as an implicit recommendation system"

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"
    - "extending detection with forensic information"
    - "patient driven privacy control through generalized distillation"

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"
    - "extending detection with forensic information"
    - "patient driven privacy control through generalized distillation"
    - "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 "privacy preserving architectures for collaborative intrusion detection"):
  with members:
    - "building and measuring privacy preserving predictive blacklists"
    - "haze privacy preserving real time traffic statistics"

Connected through hyperedge HE-5 (induced by source paper "efficient private statistics with succinct sketches"):
  with members:
    - "anticipatory mobile computing a survey of the state of the art and research challenges"
    - "extended capabilities for a privacy enhanced participatory sensing infrastructure pepsi"
    - "distributed private heavy hitters"
    - "calibrating data to sensitivity in private data analysis"
    - "predictive blacklisting as an implicit recommendation system"

Connected through hyperedge HE-6 (induced by source paper "building and measuring privacy preserving predictive blacklists"):
  with members:
    - "predictive blacklisting as an implicit recommendation system"
    - "evaluating the utility of anonymized network traces for intrusion detection"

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)