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: "optimal differentially private mechanisms for randomised response"
  Abstract: We examine a generalised randomised response (RR) technique in the context of differential privacy and examine the optimality of such mechanisms. Strict and relaxed differential privacy are considered for binary outputs. By examining the error of a statistical estimator, we present closed solutions for the optimal mechanism(s) in both cases. The optimal mechanism is also given for the specific case of the original RR technique as introduced by Warner in 1965.

Connected through hyperedge HE-1 (induced by source paper "private rank aggregation under local differential privacy"):
  with members:
    - "practical locally private heavy hitters"
    - "the application of differential privacy for rank aggregation privacy and accuracy"

Connected through hyperedge HE-2 (induced by source paper "k epsilon anonymity k anonymity with epsilon differential privacy"):
  with members:
    - "differentially private response mechanisms on categorical data"
    - "from t closeness to differential privacy and vice versa in data anonymization"
    - "on sampling anonymization and differential privacy or k anonymization meets differential privacy"

Connected through hyperedge HE-3 (induced by source paper "lopub high dimensional crowdsourced data publication with local differential privacy"):
  with members:
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "privacy in the genomic era"
    - "extremal mechanisms for local differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "on sampling anonymization and differential privacy or k anonymization meets differential privacy"

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)