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: "locally differentially private heavy hitter identification"
  Abstract: The notion of Local Differential Privacy (LDP) enables users to answer sensitive questions while preserving their privacy. The basic LDP frequent oracle protocol enables the aggregator to estimate the frequency of any value. But when the domain of input values is large, finding the most frequent values, also known as the heavy hitters, by estimating the frequencies of all possible values, is computationally infeasible. In this paper, we propose an LDP protocol for identifying heavy hitters. In our proposed protocol, which we call Prefix Extending Method (PEM), users are divided into groups, with each group reporting a prefix of her value. We analyze how to choose optimal parameters for the protocol and identify two design principles for designing LDP protocols with high utility. Experiments on both synthetic and real-world datasets demonstrate the advantage of our proposed protocol.

Connected through hyperedge HE-1 (induced by source paper "crypt epsilon crypto assisted differential privacy on untrusted servers"):
  with members:
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "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"
    - "renyi differential privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "understanding the sparse vector technique for differential privacy"

Connected through hyperedge HE-2 (induced by source paper "outis crypto assisted differential privacy on untrusted servers"):
  with members:
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "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"
    - "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-3 (induced by source paper "locally differentially private data collection and analysis"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "linear queries estimation with local differential privacy"
    - "locally private gaussian estimation"
    - "locally private mean estimation z test and tight confidence intervals"
    - "differentially private mixture of generative neural networks"
    - "renyi differential privacy"
    - "heavy hitters and the structure of local privacy"

Connected through hyperedge HE-4 (induced by source paper "collecting and analyzing multidimensional data with local differential privacy"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "practical locally private heavy hitters"
    - "marginal release under local differential privacy"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "clustering algorithms for the centralized and local models"
    - "deeprotect enabling inference based access control on mobile sensing applications"
    - "heavy hitters and the structure of local privacy"

Connected through hyperedge HE-5 (induced by source paper "locally differentially private frequency estimation with consistency"):
  with members:
    - "estimating numerical distributions under local differential privacy"
    - "pckv locally differentially private correlated key value data collection with optimized utility"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "calibrate frequency estimation and heavy hitter identification with local differential privacy via incorporating prior knowledge"
    - "linear queries estimation with local differential privacy"
    - "local differential privacy for evolving data"
    - "practical locally private heavy hitters"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"

Connected through hyperedge HE-6 (induced by source paper "calibrate frequency estimation and heavy hitter identification with local differential privacy via incorporating prior knowledge"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "practical locally private heavy hitters"
    - "optimal schemes for discrete distribution estimation under locally 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"
    - "local private efficient protocols for succinct histograms"

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)