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: "an economic analysis of privacy protection and statistical accuracy as social choices"
  Abstract: Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from U.S. statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy.

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"
    - "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"
    - "synthesizing coupling proofs of differential privacy"

Connected through hyperedge HE-2 (induced by source paper "crypt epsilon crypto assisted differential privacy on untrusted servers"):
  with members:
    - "mvg mechanism differential privacy under matrix valued query"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "ektelo a framework for defining differentially private computations"
    - "shrinkwrap differentially private query processing in private data federations"
    - "locally differentially private heavy hitter identification"
    - "renyi differential privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "understanding the sparse vector technique for differential privacy"

Connected through hyperedge HE-3 (induced by source paper "outis crypto assisted differential privacy on untrusted servers"):
  with members:
    - "mvg mechanism differential privacy under matrix valued query"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "ektelo a framework for defining differentially private computations"
    - "shrinkwrap differentially private query processing in private data federations"
    - "locally differentially private heavy hitter identification"
    - "marginal release under local differential privacy"
    - "cryptodl deep neural networks over encrypted data"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-4 (induced by source paper "fair decision making using privacy protected data"):
  with members:
    - "differential privacy has disparate impact on model accuracy"
    - "differentially private bayesian linear regression"
    - "ektelo a framework for defining differentially private computations"
    - "differentially private bayesian inference for exponential families"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "a data and workload aware algorithm for range queries under differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "issues encountered deploying differential privacy"):
  with members:
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "calibrating data to sensitivity in private data analysis"

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)