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: "collect at once use effectively making non interactive locally private learning possible"
  Abstract: Non-interactive Local Differential Privacy (LDP) requires data analysts to collect data from users through noisy channel at once. In this paper, we extend the frontiers of Non-interactive LDP learning and estimation from several aspects. For learning with smooth generalized linear losses, we propose an approximate stochastic gradient oracle estimated from non-interactive LDP channel, using Chebyshev expansion. Combined with inexact gradient methods, we obtain an efficient algorithm with quasi-polynomial sample complexity bound. For the high-dimensional world, we discover that under $\ell_2$-norm assumption on data points, high-dimensional sparse linear regression and mean estimation can be achieved with logarithmic dependence on dimension, using random projection and approximate recovery. We also extend our methods to Kernel Ridge Regression. Our work is the first one that makes learning and estimation possible for a broad range of learning tasks under non-interactive LDP model.

Connected through hyperedge HE-1 (induced by source paper "differentially private erm based on data perturbation"):
  with members:
    - "generalization in generative adversarial networks a novel perspective from privacy protection"
    - "differentially private bayesian linear regression"
    - "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "efficient private erm for smooth objectives"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-2 (induced by source paper "interaction is necessary for distributed learning with privacy or communication constraints"):
  with members:
    - "inference under information constraints ii communication constraints and shared randomness"
    - "distributed learning with sublinear communication"
    - "locally private learning without interaction requires separation"
    - "estimating smooth glm in non interactive local differential privacy model with public unlabeled data"
    - "exponential separations in local differential privacy through communication complexity"
    - "the role of interactivity in local differential privacy"
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "learning without interaction requires separation"

Connected through hyperedge HE-3 (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"
    - "calibrate frequency estimation and heavy hitter identification with local differential privacy via incorporating prior knowledge"
    - "answering range queries under local differential privacy"
    - "local differential privacy for evolving data"
    - "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-4 (induced by source paper "answering range queries under local differential privacy"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "practical locally private heavy hitters"
    - "collecting telemetry data privately"
    - "collecting and analyzing data from smart device users with local differential privacy"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "local private efficient protocols for succinct histograms"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "differentially private grids for geospatial data"

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.LG(Machine Learning)