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 "graph based nearest neighbor search from practice to theory"):
  Abstract: Graph-based approaches are empirically shown to be very successful for approximate nearest neighbor (ANN) search. However, there has been very little research on their theoretical guarantees. In this work, we consider both low-dimensional (d  > log(n)) regimes and rigorously analyze the performance of graph-based nearest neighbor algorithms when the dataset is uniformly distributed on a d-dimensional sphere. For both regimes, we provide the conditions which guarantee that a graph-based algorithm solves the ANN problem in just one iteration. In the low-dimensional regime, we also show that it is possible to solve the exact nearest neighbor problem. Finally, we discuss how the "small-world" property affects the performance of graph-based approaches.

Includes members:
  Member "graph based nearest neighbor search promises and failures":
    also connected through other hyperedges:
      (none)
  Member "optimization of indexing based on k nearest neighbor graph for proximity search in high dimensional data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "situating sentence embedders with nearest neighbor overlap")
  Member "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "graph based time space trade offs for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate similarity search under edit distance using locality sensitive hashing")
  Member "fast approximate nearest neighbor search with navigating spreading out graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "tight lower bounds for data dependent locality sensitive hashing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "optimal data dependent hashing for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "autoblock a hands off blocking framework for entity matching")
  Member "optimal lower bounds for locality sensitive hashing except when q is tiny":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep hashing methods")

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)