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: "intrinsic dimension of a dataset what properties does one expect"
  Abstract: We propose an axiomatic approach to the concept of an intrinsic dimension of a dataset, based on a viewpoint of geometry of high-dimensional structures. Our first axiom postulates that high values of dimension be indicative of the presence of the curse of dimensionality (in a certain precise mathematical sense). The second axiom requires the dimension to depend smoothly on a distance between datasets (so that the dimension of a dataset and that of an approximating principal manifold would be close to each other). The third axiom is a normalization condition: the dimension of the Euclidean n-sphere Sn is Theta(n). We give an example of a dimension function satisfying our axioms, even though it is in general computationally unfeasible, and discuss a computationally cheap function satisfying most but not all of our axioms (the "intrinsic dimensionality" of Chavez et al.)

Connected through hyperedge HE-1 (induced by source paper "intrinsic dimension of concept lattices"):
  with members:
    - "an axiomatic approach to intrinsic dimension of a dataset"
    - "on the geometry of similarity search dimensionality curse and concentration of measure"

Connected through hyperedge HE-2 (induced by source paper "intrinsic dimension and its application to association rules"):
  with members:
    - "intrinsic dimension of concept lattices"

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"
    - "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 "curse of dimensionality in the application of pivot based indexes to the similarity search problem"):
  with members:
    - "on the geometry of similarity search dimensionality curse and concentration of measure"

Connected through hyperedge HE-5 (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"
    - "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.LG(Machine Learning)