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: "a gentle introduction to the kernel distance"
  Abstract: This document reviews the definition of the kernel distance, providing a gentle introduction tailored to a reader with background in theoretical computer science, but limited exposure to technology more common to machine learning, functional analysis and geometric measure theory. The key aspect of the kernel distance developed here is its interpretation as an L2 distance between probability measures or various shapes (e.g. point sets, curves, surfaces) embedded in a vector space (specifically an RKHS). This structure enables several elegant and efficient solutions to data analysis problems. We conclude with a glimpse into the mathematical underpinnings of this measure, highlighting its recent independent evolution in two separate fields.

Connected through hyperedge HE-1 (induced by source paper "the gaussiansketch for almost relative error kernel distance"):
  with members:
    - "hashing based estimators for kernel density in high dimensions"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "locality sensitive hashing of curves"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "is input sparsity time possible for kernel low rank approximation"
    - "optimality of the johnson lindenstrauss lemma"
    - "streaming kernel principal component analysis"
    - "fastmmd ensemble of circular discrepancy for efficient two sample test"

Connected through hyperedge HE-2 (induced by source paper "improving vehicle re identification using cnn latent spaces metrics comparison and track to track extension"):
  with members:
    - "ram a region aware deep model for vehicle re identification"
    - "vehicle re identification using quadruple directional deep learning features"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "sift meets cnn a decade survey of instance retrieval"
    - "a survey of sparse representation algorithms and applications"
    - "a large scale car dataset for fine grained categorization and verification"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "near optimal coresets of kernel density estimates"):
  with members:
    - "improved coresets for kernel density estimates"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "geometric inference on kernel density estimates"
    - "on the equivalence between herding and conditional gradient algorithms"
    - "algorithms for eps approximations of terrains"

Connected through hyperedge HE-4 (induced by source paper "unsupervised adaptive re identification in open world dynamic camera networks"):
  with members:
    - "person re identification via recurrent feature aggregation"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"
    - "personnet person re identification with deep convolutional neural networks"
    - "continuous adaptation of multi camera person identification models through sparse non redundant representative selection"
    - "person re identification past present and future"
    - "end to end deep learning for person search"
    - "human in the loop person re identification"

Connected through hyperedge HE-5 (induced by source paper "improved coresets for kernel density estimates"):
  with members:
    - "algorithmic discrepancy beyond partial coloring"
    - "geometric inference on kernel density estimates"

Connected through hyperedge HE-6 (induced by source paper "geometric inference on kernel density estimates"):
  with members:
    - "introduction to the r package tda"
    - "efficient and robust persistent homology for measures"
    - "linear size approximations to the vietoris rips filtration"
    - "witnessed k distance"
    - "boundary measures for geometric inference"
    - "computing multidimensional persistence"
    - "stability of curvature measures"
    - "towards persistence based reconstruction in euclidean spaces"

Connected through hyperedge HE-7 (induced by source paper "a multi scale spatial model for rss based device free localization"):
  with members:
    - "nuzzer a large scale device free passive localization system for wireless environments"

Connected through hyperedge HE-8 (induced by source paper "matching shapes using the current distance"):
  with members:
    - "constructive algorithms for discrepancy minimization"

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.CG(Computational Geometry)