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: "cipher construction of differentially private microdata from low dimensional histograms via solving linear equations with tikhonov regularization"
  Abstract: When government agencies, research institutes, industries release individual-level data for research and public use, the data is often perturbed in certain ways to provide some level of privacy protection. The recently developed differentially private data synthesis (DIPS) methods are built upon the concept of differential privacy and provides a strong mathematical privacy guarantee while aiming to maintain the statistical utility of the released sanitized data. We introduce a new DIPS algorithm, CIPHER, which generates differential private individual-level data from a set of low-dimensional histograms via solving a set of linear equations with the Tikhonov ($l_2$) regularization. CIPHER is conceptually very simple and requires nothing than decomposing joint probabilities via basic provability rules to construct the equation set and subsequently solving linear equations. CIPHER also has the ability to automatically "correct" for the inconsistency arising from the differential private sanitization among the histograms that share at least one common attributes. We compare CIPHER with the MWEM (multiplicative weighting via exponential mechanism) and the full-dimensional histogram (FDH) sanitization through simulation and case studies. The results demonstrate that CIPHER made significance improvements over MWEM in statistical inferences, both of which aim to generate differentially private synthetic individual-level data from a set of low-dimensional histograms. CIPHER also delivered similar performance as the FDH sanitation for most of the examined privacy budget range.

Connected through hyperedge HE-1 (induced by source paper "graphical model based estimation and inference for differential privacy"):
  with members:
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "differentially private learning of undirected graphical models using collective graphical models"
    - "a data and workload aware algorithm for range queries under differential privacy"
    - "dual query practical private query release for high dimensional data"
    - "calibrating data to sensitivity in private data analysis"
    - "faster algorithms for privately releasing marginals"
    - "differentially private grids for geospatial data"
    - "privately releasing conjunctions and the statistical query barrier"

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)