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: "on the semantics of differential privacy a bayesian formulation"
  Abstract: Differential privacy is a definition of "privacy'" for algorithms that analyze and publish information about statistical databases. It is often claimed that differential privacy provides guarantees against adversaries with arbitrary side information. In this paper, we provide a precise formulation of these guarantees in terms of the inferences drawn by a Bayesian adversary. We show that this formulation is satisfied by both "vanilla" differential privacy as well as a relaxation known as (epsilon,delta)-differential privacy. Our formulation follows the ideas originally due to Dwork and McSherry [Dwork 2006]. This paper is, to our knowledge, the first place such a formulation appears explicitly. The analysis of the relaxed definition is new to this paper, and provides some concrete guidance for setting parameters when using (epsilon,delta)-differential privacy.

Connected through hyperedge HE-1 (induced by source paper "guidelines for implementing and auditing differentially private systems"):
  with members:
    - "differentially private objective perturbation beyond smoothness and convexity"
    - "identifying cache based side channels through secret augmented abstract interpretation"
    - "fuzzi a three level logic for differential privacy"
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "private selection from private candidates"
    - "ektelo a framework for defining differentially private computations"
    - "detecting violations of differential privacy"
    - "how to use heuristics for differential privacy"

Connected through hyperedge HE-2 (induced by source paper "a necessary and sufficient stability notion for adaptive generalization"):
  with members:
    - "natural analysts in adaptive data analysis"
    - "an operational approach to information leakage"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "calibrating noise to variance in adaptive data analysis"
    - "learners that use little information"
    - "learners that leak little information"
    - "typicality based stability and privacy"
    - "inferential privacy guarantees for differentially private mechanisms"

Connected through hyperedge HE-3 (induced by source paper "secure and utility aware data collection with condensed local differential privacy"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "detecting violations of differential privacy"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "practical locally private heavy hitters"
    - "marginal release under local differential privacy"
    - "heavy hitters and the structure of local privacy"
    - "collecting telemetry data privately"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"

Connected through hyperedge HE-4 (induced by source paper "hypothesis testing interpretations and renyi differential privacy"):
  with members:
    - "gaussian differential privacy"
    - "issues encountered deploying differential privacy"
    - "detecting violations of differential privacy"
    - "towards practical differential privacy for sql queries"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "approximate relational hoare logic for continuous random samplings"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "differential privacy an economic method for choosing epsilon"

Connected through hyperedge HE-5 (induced by source paper "robustness of maximal alpha leakage to side information"):
  with members:
    - "an operational approach to information leakage"
    - "a tunable measure for information leakage"
    - "privacy aware guessing efficiency"
    - "renyi differential privacy"

Connected through hyperedge HE-6 (induced by source paper "the dirichlet mechanism for differential privacy on the unit simplex"):
  with members:
    - "towards differential privacy for symbolic systems"
    - "differential privacy an economic method for choosing epsilon"
    - "privacy preserving record linkage via grams projections"

Connected through hyperedge HE-7 (induced by source paper "distributed differential privacy via shuffling"):
  with members:
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "tight lower bounds for locally differentially private selection"
    - "the price of selection in differential privacy"
    - "prio private robust and scalable computation of aggregate statistics"
    - "tight lower bounds for differentially private selection"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "local private efficient protocols for succinct histograms"

Connected through hyperedge HE-8 (induced by source paper "data privacy for a rho recoverable function"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "context aware generative adversarial privacy"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "local private efficient protocols for succinct histograms"
    - "renyi divergence and kullback leibler divergence"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "the composition theorem for differential privacy"
    - "privacy against statistical inference"

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)