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 in datasets of short protein fragments"
  Abstract: We propose a family of very efficient hierarchical indexing schemes for ungapped, score matrix-based similarity search in large datasets of short (4-12 amino acid) protein fragments. This type of similarity search has importance in both providing a building block to more complex algorithms and for possible use in direct biological investigations where datasets are of the order of 60 million objects. Our scheme is based on the internal geometry of the amino acid alphabet and performs exceptionally well, for example outputting 100 nearest neighbours to any possible fragment of length 10 after scanning on average less than one per cent of the entire dataset.

Connected through hyperedge HE-1 (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 an illustrated paradigm"
    - "on the geometry of similarity search dimensionality curse and concentration of measure"

Connected through hyperedge HE-2 (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 an illustrated paradigm"
    - "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)