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 "distance sensitive hashing"):
  Abstract: Locality-sensitive hashing (LSH) is an important tool for managing high-dimensional noisy or uncertain data, for example in connection with data cleaning (similarity join) and noise-robust search (similarity search). However, for a number of problems the LSH framework is not known to yield good solutions, and instead ad hoc solutions have been designed for particular similarity and distance measures. For example, this is true for output-sensitive similarity search/join, and for indexes supporting annulus queries that aim to report a point close to a certain given distance from the query point. #R##N#In this paper we initiate the study of distance-sensitive hashing (DSH), a generalization of LSH that seeks a family of hash functions such that the probability of two points having the same hash value is a given function of the distance between them. More precisely, given a distance space $(X, \text{dist})$ and a "collision probability function" (CPF) $f\colon \mathbb{R}\rightarrow [0,1]$ we seek a distribution over pairs of functions $(h,g)$ such that for every pair of points $x, y \in X$ the collision probability is $\Pr[h(x)=g(y)] = f(\text{dist}(x,y))$. Locality-sensitive hashing is the study of how fast a CPF can decrease as the distance grows. For many spaces, $f$ can be made exponentially decreasing even if we restrict attention to the symmetric case where $g=h$. We show that the asymmetry achieved by having a pair of functions makes it possible to achieve CPFs that are, for example, increasing or unimodal, and show how this leads to principled solutions to problems not addressed by the LSH framework. This includes a novel application to privacy-preserving distance estimation. We believe that the DSH framework will find further applications in high-dimensional data management.

Includes members:
  Member "approximate furthest neighbor with application to annulus query":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "similarity problems in high dimensions")
  Member "embedjoin efficient edit similarity joins via embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "parameter free locality sensitive hashing for spherical range reporting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating text to pattern hamming distances")
  Member "sub linear privacy preserving near neighbor search with untrusted server on large scale datasets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fair near neighbor search independent range sampling in high dimensions")
  Member "a framework for similarity search with space time tradeoffs using locality sensitive filtering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew")
  Member "optimal hashing based time space trade offs for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the i o complexity of the k nearest neighbor problem")
  Member "on the complexity of inner product similarity join":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew")
  Member "set similarity search beyond minhash":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blocking and filtering techniques for entity resolution a survey")

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)