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: "calibrate frequency estimation and heavy hitter identification with local differential privacy via incorporating prior knowledge"
  Abstract: Estimating frequencies of certain items among a population is a basic step in data analytics, which enables more advanced data analytics (e.g., heavy hitter identification, frequent pattern mining), client software optimization, and detecting unwanted or malicious hijacking of user settings in browsers. Frequency estimation and heavy hitter identification with local differential privacy (LDP) protect user privacy as well as the data collector. Existing LDP algorithms cannot leverage 1) prior knowledge about the noise in the estimated item frequencies and 2) prior knowledge about the true item frequencies. As a result, they achieve suboptimal performance in practice. #R##N#In this work, we aim to design LDP algorithms that can leverage such prior knowledge. Specifically, we design ${Calibrate}$ to incorporate the prior knowledge via statistical inference. ${Calibrate}$ can be appended to an existing LDP algorithm to reduce its estimation errors. We model the prior knowledge about the noise and the true item frequencies as two probability distributions, respectively. Given the two probability distributions and an estimated frequency of an item produced by an existing LDP algorithm, our ${Calibrate}$ computes the conditional probability distribution of the item's frequency and uses the mean of the conditional probability distribution as the calibrated frequency for the item. It is challenging to estimate the two probability distributions due to data sparsity. We address the challenge via integrating techniques from statistics and machine learning. Our empirical results on two real-world datasets show that ${Calibrate}$ significantly outperforms state-of-the-art LDP algorithms for frequency estimation and heavy hitter identification.

Connected through hyperedge HE-1 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "generative adversarial privacy"
    - "poisoning attacks to graph based recommender systems"
    - "graph based security and privacy analytics via collective classification with joint weight learning and propagation"
    - "adversarial binaries for authorship identification"

Connected through hyperedge HE-2 (induced by source paper "blender enabling local search with a hybrid differential privacy model"):
  with members:
    - "privacy amplification by iteration"
    - "the power of the hybrid model for mean estimation"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "practical locally private heavy hitters"
    - "differentially private identity and closeness testing of discrete distributions"
    - "differentially private testing of identity and closeness of discrete distributions"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "local private efficient protocols for succinct histograms"

Connected through hyperedge HE-3 (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"
    - "linear queries estimation with local differential privacy"
    - "local differential privacy for evolving data"
    - "practical locally private heavy hitters"
    - "locally differentially private heavy hitter identification"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"

Connected through hyperedge HE-4 (induced by source paper "consistent and accurate frequency oracles under local differential privacy"):
  with members:
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "answering range queries under local differential privacy"
    - "local differential privacy for evolving data"
    - "collect at once use effectively making non interactive locally private learning possible"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "heavy hitters and the structure of local privacy"
    - "collecting telemetry data privately"

Connected through hyperedge HE-5 (induced by source paper "four accuracy bounds and one estimator for frequency estimation under local differential privacy"):
  with members:
    - "information theoretic metrics for local differential privacy protocols"
    - "consistent and accurate frequency oracles under local differential privacy"
    - "towards instance optimal private query release"
    - "collecting telemetry data privately"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "extremal mechanisms for local differential privacy"
    - "what can we learn privately"

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)