human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "differentially private m band wavelet based mechanisms in machine learning environments"):
  Abstract: In the post-industrial world, data science and analytics have gained paramount importance regarding digital data privacy. Improper methods of establishing privacy for accessible datasets can compromise large amounts of user data even if the adversary has a small amount of preliminary knowledge of a user. Many researchers have been developing high-level privacy-preserving mechanisms that also retain the statistical integrity of the data to apply to machine learning. Recent developments of differential privacy, such as the Laplace and Privelet mechanisms, drastically decrease the probability that an adversary can distinguish the elements in a data set and thus extract user information. In this paper, we develop three privacy-preserving mechanisms with the discrete M-band wavelet transform that embed noise into data. The first two methods (LS and LS+) add noise through a Laplace-Sigmoid distribution that multiplies Laplace-distributed values with the sigmoid function, and the third method utilizes pseudo-quantum steganography to embed noise into the data. We then show that our mechanisms successfully retain both differential privacy and learnability through statistical analysis in various machine learning environments.

Includes members:
  Member "benefits and pitfalls of the exponential mechanism with applications to hilbert spaces and functional pca":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "elliptical perturbations for differential privacy")
  Member "pain free random differential privacy with sensitivity sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differential privacy at risk bridging randomness and privacy budget")
  Member "a data and workload aware algorithm for range queries under differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "data dependent differentially private parameter learning for directed graphical models")
  Member "differential privacy and machine learning a survey and review":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy for all demystify vulnerability disparity of differential privacy against membership inference attack")
  Member "differential privacy via wavelet transforms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the power of multiple anonymous messages")
  Member "boosting the accuracy of differentially private histograms through consistency":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")

Question: Please tell me which class the source paper 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)