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: "indexing schemes for similarity search an illustrated paradigm"
  Abstract: We suggest a variation of the Hellerstein--Koutsoupias--Papadimitriou indexability model for datasets equipped with a similarity measure, with the aim of better understanding the structure of indexing schemes for similarity-based search and the geometry of similarity workloads. This in particular provides a unified approach to a great variety of schemes used to index into metric spaces and facilitates their transfer to more general similarity measures such as quasi-metrics. We discuss links between performance of indexing schemes and high-dimensional geometry. The concepts and results are illustrated on a very large concrete dataset of peptide fragments equipped with a biologically significant similarity measure.

Connected through hyperedge HE-1 (induced by source paper "on the i o complexity of the k nearest neighbor problem"):
  with members:
    - "approximate nearest neighbor search in high dimensions"
    - "hardness of approximate nearest neighbor search"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "neural codes for image retrieval"
    - "efficient distributed locality sensitive hashing"
    - "lower bounds on near neighbor search via metric expansion"
    - "lower bounds on performance of metric tree indexing schemes for exact similarity search in high dimensions"
    - "dynamic external hashing the limit of buffering"

Connected through hyperedge HE-2 (induced by source paper "comparison based indexing from first principles"):
  with members:
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"

Connected through hyperedge HE-3 (induced by source paper "indexability concentration and vc theory"):
  with members:
    - "curse of dimensionality in pivot based indexes"
    - "lower bounds on performance of metric tree indexing schemes for exact similarity search in high dimensions"
    - "intrinsic dimension of a dataset what properties does one expect"
    - "an axiomatic approach to intrinsic dimension of a dataset"
    - "indexing schemes for similarity search in datasets of short protein fragments"
    - "on the geometry of similarity search dimensionality curse and concentration of measure"

Connected through hyperedge HE-4 (induced by source paper "lower bounds on performance of metric tree indexing schemes for exact similarity search in high dimensions"):
  with members:
    - "indexability concentration and vc theory"
    - "lower bounds on near neighbor search via metric expansion"
    - "curse of dimensionality in pivot based indexes"
    - "intrinsic dimension of a dataset what properties does one expect"
    - "an axiomatic approach to intrinsic dimension of a dataset"
    - "indexing schemes for similarity search in datasets of short protein fragments"
    - "on the geometry of similarity search dimensionality curse and concentration of measure"

Connected through hyperedge HE-5 (induced by source paper "intrinsic dimension of a dataset what properties does one expect"):
  with members:
    - "what is the dimension of your binary data"
    - "on the geometry of similarity search dimensionality curse and concentration of measure"
    - "a geometric framework for modelling similarity search"

Connected through hyperedge HE-6 (induced by source paper "indexing schemes for similarity search in datasets of short protein fragments"):
  with members:
    - "on the geometry of similarity search dimensionality curse and concentration of measure"

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)