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: "espresso efficient privacy preserving evaluation of sample set similarity"
  Abstract: In today's digital society, electronic information is increasingly shared among different entities, and decisions are made based on common attributes. To address associated privacy concerns, the research community has begun to develop cryptographic techniques for controlled (privacy-preserving) information sharing. One interesting open problem involves two mutually distrustful parties that need to assess the similarity of their information sets, but cannot disclose their actual content. This paper presents the first efficient and provably-secure construction for privacy-preserving evaluation of sample set similarity, measured as the Jaccard similarity index. We present two protocols: the first securely computes the Jaccard index of two sets, the second approximates it, using MinHash techniques, with lower costs. We show that our novel protocols are attractive in many compelling applications, including document similarity, biometric authentication, genetic tests, multimedia file similarity. Finally, we demonstrate that our constructions are appreciably more efficient than prior work.

Connected through hyperedge HE-1 (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-2 (induced by source paper "controlled data sharing for collaborative predictive blacklisting"):
  with members:
    - "what s the gist privacy preserving aggregation of user profiles"
    - "predictive blacklisting as an implicit recommendation system"
    - "evaluating the utility of anonymized network traces for intrusion detection"
    - "sharing computer network logs for security and privacy a motivation for new methodologies of anonymization"

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)