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: "the cumulative distribution transform and linear pattern classification"
  Abstract: Classifying (determining the label) of data emanating from sensors is an important problem with many applications in science and technology. We describe a new transform for patterns that can be interpreted as a probability density function, that has special properties with regards to classification. The transform, which we denote as the Cumulative Distribution Transform (CDT) is invertible, with well defined forward and inverse operations. We show that it can be useful in 'parsing out' variations (confounds) that are 'Lagrangian' (displacement or transport) by converting these to 'Eulerian' variations in transform domain. This conversion is the basis for our main result that describes when the CDT can allow for linear classification to be possible in signal domain. We also describe several properties of the transform and show, with computational experiments that used both real and simulated data, that the CDT can help render a variety of real world problems simpler to solve.

Connected through hyperedge HE-1 (induced by source paper "sliced wasserstein distance for learning gaussian mixture models"):
  with members:
    - "adversarial learning for neural dialogue generation"
    - "improved training of wasserstein gans"
    - "wasserstein learning of deep generative point process models"
    - "differential geometric retrieval of deep features"
    - "sliced wasserstein kernels for probability distributions"
    - "learning with a wasserstein loss"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-2 (induced by source paper "a transportation l p distance for signal analysis"):
  with members:
    - "image labeling by assignment"
    - "transport based analysis modeling and learning from signal and data distributions"
    - "the radon cumulative distribution transform and its application to image classification"
    - "learning with a wasserstein loss"
    - "imaging with kantorovich rubinstein discrepancy"

Connected through hyperedge HE-3 (induced by source paper "the radon cumulative distribution transform and its application to image classification"):
  with members:
    - "sliced wasserstein kernels for probability distributions"
    - "pcanet a simple deep learning baseline for image classification"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-4 (induced by source paper "sliced wasserstein kernels for probability distributions"):
  with members:
    - "on the definiteness of earth mover s distance and its relation to set intersection"
    - "the radon cumulative distribution transform and its application to image classification"
    - "earth mover s distance yields positive definite kernels for certain ground distances"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "geodesic exponential kernels when curvature and linearity conflict"

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.CV(Computer Vision and Pattern Recognition)