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 "mvg mechanism differential privacy under matrix valued query"):
  Abstract: Differential privacy mechanism design has traditionally been tailored for a scalar-valued query function. Although many mechanisms such as the Laplace and Gaussian mechanisms can be extended to a matrix-valued query function by adding i.i.d. noise to each element of the matrix, this method is often suboptimal as it forfeits an opportunity to exploit the structural characteristics typically associated with matrix analysis. To address this challenge, we propose a novel differential privacy mechanism called the Matrix-Variate Gaussian (MVG) mechanism, which adds a matrix-valued noise drawn from a matrix-variate Gaussian distribution, and we rigorously prove that the MVG mechanism preserves (e,δ)-differential privacy. Furthermore, we introduce the concept of directional noise made possible by the design of the MVG mechanism. Directional noise allows the impact of the noise on the utility of the matrix-valued query function to be moderated. Finally, we experimentally demonstrate the performance of our mechanism using three matrix-valued queries on three privacy-sensitive datasets. We find that the MVG mechanism can notably outperforms four previous state-of-the-art approaches, and provides comparable utility to the non-private baseline.

Includes members:
  Member "a differential privacy mechanism design under matrix valued query":
    also connected through other hyperedges:
      (none)
  Member "towards practical differential privacy for sql queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "linkedin s audience engagements api a privacy preserving data analytics system at scale")
  Member "coupling random orthonormal projection with gaussian generative model for non interactive private data release":
    also connected through other hyperedges:
      (none)
  Member "convex optimization for linear query processing under approximate differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "understanding the sparse vector technique for differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach")
  Member "principled evaluation of differentially private algorithms using dpbench":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "optimizing batch linear queries under exact and approximate differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crypt epsilon crypto assisted differential privacy on untrusted servers")
  Member "an improved private mechanism for small databases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the power of factorization mechanisms in local and central 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.CR(Cryptography and Security)