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: "curse of dimensionality in pivot based indexes"
  Abstract: We offer a theoretical validation of the curse of dimensionality in the pivot-based indexing of datasets for similarity search, by proving, in the framework of statistical learning, that in high dimensions no pivot-based indexing scheme can essentially outperform the linear scan. A study of the asymptotic performance of pivot-based indexing schemes is performed on a sequence of datasets modeled as samples picked in i.i.d. fashion from a sequence of metric spaces. We allow the size of the dataset to grow in relation to dimension, such that the dimension is superlogarithmic but subpolynomial in the size of the dataset. The number of pivots is sublinear in the size of the dataset. We pick the least restrictive cost model of similarity search where we count each distance calculation as a single computation and disregard the rest. We demonstrate that if the intrinsic dimension of the spaces in the sense of concentration of measure phenomenon is linear in dimension, then the performance of similarity search pivot-based indexes is asymptotically linear in the size of the dataset.

Connected through hyperedge HE-1 (induced by source paper "gpu accelerated similarity self join for multi dimensional data"):
  with members:
    - "gpu accelerated self join for the distance similarity metric"

Connected through hyperedge HE-2 (induced by source paper "maximum likelihood approximate nearest neighbor method in real time image recognition"):
  with members:
    - "deep learning in neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "indexability concentration and vc theory"):
  with members:
    - "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"
    - "indexing schemes for similarity search an illustrated paradigm"
    - "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"
    - "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"
    - "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)