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: "differentially private multi party computation optimality of non interactive randomized response"
  Abstract: We study the problem of interactive function computation by multiple parties possessing a single bit each in a differential privacy setting (i.e., there remains an uncertainty in any specific party's bit even when given the transcript of the interactions and all the other parties' bits). Each party is interested in computing a function, which could differ from party to party, and there could be a central observer interested in computing a separate function. Performance at each party and the central observer is measured via the accuracy of the function computed. We allow for an arbitrary cost function to measure the distortion between the true and the computed function value. Our main result is the exact optimality of a simple non-interactive protocol: each party randomizes (sufficiently) and publishes its own bit. In other words, non-interactive randomized response is exactly optimal. Each party and the central observer then separately compute their respective function to maximize the appropriate notion of their accuracy measure. The optimality is very general: it holds for all types of functions, heterogeneous privacy conditions on the parties, all types of cost metrics, and both average and worst-case (over the inputs) measures of accuracy. Finally, the optimality result is simultaneous, in terms of maximizing accuracy at each of the parties and the central observer.

Connected through hyperedge HE-1 (induced by source paper "marginal release under local differential privacy"):
  with members:
    - "collecting and analyzing data from smart device users with local differential privacy"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "analysis of boolean functions"
    - "extremal mechanisms for local differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "faster algorithms for privately releasing marginals"
    - "a simple and practical algorithm for differentially private data release"

Connected through hyperedge HE-2 (induced by source paper "collecting and analyzing data from smart device users with local differential privacy"):
  with members:
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "crowd ml a privacy preserving learning framework for a crowd of smart devices"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-3 (induced by source paper "extremal mechanisms for local differential privacy"):
  with members:
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "the optimal mechanism in differential privacy multidimensional setting"
    - "optimal noise adding mechanisms for approximate differential privacy"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "the composition theorem for differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "the optimal mechanism in differential privacy"
    - "convergence rates for differentially private statistical estimation"

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)