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 "sub linear privacy preserving search with untrusted server and semi honest parties"):
  Abstract: Privacy-preserving Near-neighbor search (PP-NNS) is a well-studied problem in the literature. The overwhelming growth in the size of current datasets and the lack of any truly secure server in the online world render the existing solutions impractical either due to their high computational requirements or the non-realistic assumptions which potentially compromise privacy. PP-NNS with multiple (semi-honest) data owners having query time sub-linear in the number of users has been proposed as an open research direction. In this paper, we provide the first such algorithm which has a sub-linear query time and the ability to handle semi-honest (honest but curious) parties. Our algorithm can further manage the situation where a large chunk of the server information is being compromised. Probabilistic embedding based on Locality Sensitive Hashing (LSH) is the algorithm of choice for sub-linear near-neighbor search in high dimensions. However, we show that LSH is not suitable for semi-honest setting, and particularly when the server information is compromisable. LSH allows estimation of any pairwise distances between users, which can be easily compromised to learn user attributes using the idea of "triangulation". We suggest a novel methodology which overcomes this LSH vulnerability. At the heart of our proposal lies a secure probabilistic embedding scheme generated from a novel probabilistic transformation over appropriate LSH family. Our secure embeddings combined with advances in multi-party computation result in an efficient PP-NNS algorithm suitable for massive-scale datasets without strong assumptions on the behavior of parties involved. We demonstrate the validity of our claims by experimentally showing the effectiveness of our solution in hiding the sensitive variables in medical records without compromising the precision-recall of the retrieval.

Includes members:
  Member "riposte an anonymous messaging system handling millions of users":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "prifi low latency anonymity for organizational networks")
  Member "exploring privacy preservation in outsourced k nearest neighbors with multiple data owners":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sub linear privacy preserving near neighbor search")
  Member "privacy via the johnson lindenstrauss transform":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interpretable and differentially private predictions")
  Member "coding for random projections":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "locality sensitive hashing for f divergences mutual information loss and beyond")
  Member "secure k nearest neighbor query over encrypted data in outsourced environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic skyline queries on encrypted data using result materialization")
  Member "iterative hard thresholding for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

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)