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 "privacy via the johnson lindenstrauss transform"):
  Abstract: Suppose that party A collects private information about its users, where each user's data is represented as a bit vector. Suppose that party B has a proprietary data mining algorithm that requires estimating the distance between users, such as clustering or nearest neighbors. We ask if it is possible for party A to publish some information about each user so that B can estimate the distance between users without being able to infer any private bit of a user. Our method involves projecting each user's representation into a random, lower-dimensional space via a sparse Johnson-Lindenstrauss transform and then adding Gaussian noise to each entry of the lower-dimensional representation. We show that the method preserves differential privacy---where the more privacy is desired, the larger the variance of the Gaussian noise. Further, we show how to approximate the true distances between users via only the lower-dimensional, perturbed data. Finally, we consider other perturbation methods such as randomized response and draw comparisons to sketch-based methods. While the goal of releasing user-specific data to third parties is more broad than preserving distances, this work shows that distance computations with privacy is an achievable goal.

Includes members:
  Member "distributed private heavy hitters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the power of multiple anonymous messages")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally private hypothesis selection")
  Member "the johnson lindenstrauss transform itself preserves differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "crypt epsilon crypto assisted differential privacy on untrusted servers")
  Member "fast private data release algorithms for sparse queries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mvg mechanism differential privacy under matrix valued query")
  Member "a learning theory approach to non interactive database privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "compressive mechanism utilizing sparse representation in differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")
  Member "a sparse johnson lindenstrauss transform":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers")
  Member "privacy in search logs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dpcube differentially private histogram release through multidimensional partitioning")

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.DS(Data Structures and Algorithms)