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 "sanns scaling up secure approximate k nearest neighbors search"):
  Abstract: We present new secure protocols for approximate $k$-nearest neighbor search ($k$-NNS) over the Euclidean distance in the semi-honest model. Our implementation is able to handle massive datasets efficiently. On the algorithmic front, we show a new circuit for the approximate top-$k$ selection from $n$ numbers that is built from merely $O(n + \mathrm{poly}(k))$ comparators. Using this circuit as a subroutine, we design new approximate $k$-NNS algorithms and two corresponding secure protocols: 1) optimized linear scan; 2) clustering-based sublinear time algorithm. #R##N#Our secure protocols utilize a combination of additively-homomorphic encryption, garbled circuit and Oblivious RAM. Along the way, we introduce various optimizations to these primitives, which drastically improve concrete efficiency. #R##N#We evaluate the new protocols empirically and show that they are able to handle datasets that are significantly larger than in the prior work. For instance, running on two standard Azure instances within the same availability zone, for a dataset of $96$-dimensional descriptors of $10\,000\,000$ images, we can find $10$ nearest neighbors with average accuracy $0.9$ in under $10$ seconds improving upon prior work by at least two orders of magnitude.

Includes members:
  Member "scalable nearest neighbor search for optimal transport":
    also connected through other hyperedges:
      (none)
  Member "approximate nearest neighbor search in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "copt coordinated optimal transport on graphs")
  Member "the unreasonable effectiveness of deep features as a perceptual metric":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "scalable secure computation of statistical functions with applications to k nearest neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lower bounds for oblivious near neighbor search")
  Member "chameleon a hybrid secure computation framework for machine learning applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cryptanalytic extraction of neural network models")
  Member "gazelle a low latency framework for secure neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep quaternion features for privacy protection")
  Member "secure k ish nearest neighbors classifier":
    also connected through other hyperedges:
      (none)
  Member "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")

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)