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: "universally optimal privacy mechanisms for minimax agents"
  Abstract: A scheme that publishes aggregate information about sensitive data must resolve the trade-off between utility to information consumers and privacy of the database participants. Differential privacy is a well-established definition of privacy--this is a universal guarantee against all attackers, whatever their side-information or intent. In this paper, we present a universal treatment of utility based on the standard minimax rule from decision theory (in contrast to the utility model in, which is Bayesian). In our model, information consumers are minimax (risk-averse) agents, each possessing some side-information about the query, and each endowed with a loss-function which models their tolerance to inaccuracies. Further, information consumers are rational in the sense that they actively combine information from the mechanism with their side-information in a way that minimizes their loss. Under this assumption of rational behavior, we show that for every fixed count query, a certain geometric mechanism is universally optimal for all minimax information consumers. Additionally, our solution makes it possible to release query results at multiple levels of privacy in a collusion-resistant manner.

Connected through hyperedge HE-1 (induced by source paper "prospect theoretic analysis of privacy preserving mechanism"):
  with members:
    - "a mean field stackelberg game approach for obfuscation adoption in empirical risk minimization"
    - "managing price uncertainty in prosumer centric energy trading a prospect theoretic stackelberg game approach"
    - "reap an efficient incentive mechanism for reconciling aggregation accuracy and individual privacy in crowdsensing"
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"
    - "hardware trojan detection game a prospect theoretic approach"
    - "prospect theory for enhanced smart grid resilience using distributed energy storage"
    - "market model and optimal pricing scheme of big data and internet of things iot"
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"

Connected through hyperedge HE-2 (induced by source paper "privacy and utility tradeoff in approximate differential privacy"):
  with members:
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "locally private hypothesis testing"
    - "renyi differential privacy"
    - "dp em differentially private expectation maximization"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private online learning"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-3 (induced by source paper "differential privacy techniques for cyber physical systems a survey"):
  with members:
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "sok differential privacies"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "achieving differential privacy against non intrusive load monitoring in smart grid a fog computing approach"
    - "toward practical privacy preserving analytics for iot and cloud based healthcare systems"
    - "privacy preserving distributed deep learning for clinical data"
    - "re dpoctor real time health data releasing with w day differential privacy"

Connected through hyperedge HE-4 (induced by source paper "optimal noise functions for location privacy on continuous regions"):
  with members:
    - "optimal geo indistinguishable mechanisms for location privacy"
    - "optimal noise adding mechanisms for approximate differential privacy"
    - "a predictive differentially private mechanism for mobility traces"
    - "geo indistinguishability differential privacy for location based systems"
    - "impossibility of differentially private universally optimal mechanisms"
    - "universally utility maximizing privacy mechanisms"

Connected through hyperedge HE-5 (induced by source paper "constrained differential privacy for count data"):
  with members:
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "impossibility of differentially private universally optimal mechanisms"

Connected through hyperedge HE-6 (induced by source paper "privacy games optimal user centric data obfuscation"):
  with members:
    - "privacy tradeoffs in predictive analytics"
    - "optimal geo indistinguishable mechanisms for location privacy"
    - "blowfish privacy tuning privacy utility trade offs using policies"
    - "prolonging the hide and seek game optimal trajectory privacy for location based services"
    - "a predictive differentially private mechanism for mobility traces"
    - "geo indistinguishability differential privacy for location based systems"
    - "calibrating data to sensitivity in private data analysis"
    - "the optimal mechanism in differential privacy"

Connected through hyperedge HE-7 (induced by source paper "the geometry of differential privacy the sparse and approximate cases"):
  with members:
    - "nearly optimal private convolution"
    - "optimal private halfspace counting via discrepancy"
    - "calibrating data to sensitivity in private data analysis"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "the power of linear reconstruction attacks"
    - "a learning theory approach to non interactive database privacy"
    - "lower bounds in differential privacy"
    - "iterative constructions and private data release"

Connected through hyperedge HE-8 (induced by source paper "optimal noise adding mechanisms for approximate differential privacy"):
  with members:
    - "the geometry of differential privacy the sparse and approximate cases"
    - "the composition theorem for differential privacy"
    - "the optimal mechanism in differential privacy"
    - "differentially private online learning"
    - "lower bounds in differential privacy"
    - "impossibility of differentially private universally optimal mechanisms"
    - "on the geometry of differential privacy"
    - "boosting the accuracy of differentially private histograms through consistency"

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)