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: "scalable secure computation of statistical functions with applications to k nearest neighbors"
  Abstract: Given a set $S$ of $n$ $d$-dimensional points, the $k$-nearest neighbors (KNN) is the problem of quickly finding $k$ points in $S$ that are nearest to a query point $q$. The $k$-nearest neighbors problem has applications in machine learning for classification and regression and also in searching. The secure version of KNN where either $q$ or $S$ are encrypted, has applications such as providing services over sensitive (such as medical or localization) data. #R##N#In this work we present the first scalable and efficient algorithm for solving KNN with Fully Homomorphic Encryption (FHE) that is realized by a polynomial whose degree is independent of $n$, the number of points. We implemented our algorithm in an open source library based on HELib implementation for the Brakerski-Gentry-Vakuntanthan's FHE scheme, and ran experiments on MIT's OpenStack cloud. Our experiments show that given a query point $q$, we can find the set of 20 nearest points out of more than 1000 points in less than an hour. #R##N#Our result introduces a statistical coreset, which is a data summarization technique that allows statistical functions, such as moments, to be efficiently and scalably computed. As a central tool, we design a new coin toss technique which we use to build the coreset. This coin toss technique and computation of statistical functions may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "sanns scaling up secure approximate k nearest neighbors search"):
  with members:
    - "scalable nearest neighbor search for optimal transport"
    - "approximate nearest neighbor search in high dimensions"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "gazelle a low latency framework for secure neural network inference"
    - "secure k ish nearest neighbors classifier"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "billion scale similarity search with gpus"

Connected through hyperedge HE-2 (induced by source paper "lower bounds for oblivious near neighbor search"):
  with members:
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "hardness of approximate nearest neighbor search"
    - "lower bounds for oblivious data structures"
    - "distributed pcp theorems for hardness of approximation in p"
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"
    - "new unconditional hardness results for dynamic and online problems"
    - "secure k nearest neighbor query over encrypted data in outsourced environments"
    - "the cell probe complexity of dynamic range counting"

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.DS(Data Structures and Algorithms)