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: "classification with scattering operators"
  Abstract: A scattering vector is a local descriptor including multiscale and multi-direction co-occurrence information. It is computed with a cascade of wavelet decompositions and complex modulus. This scattering representation is locally translation invariant and linearizes deformations. A supervised classification algorithm is computed with a PCA model selection on scattering vectors. State of the art results are obtained for handwritten digit recognition and texture classification.

Connected through hyperedge HE-1 (induced by source paper "geometric scattering for graph data analysis"):
  with members:
    - "graph convolutional neural networks via scattering"
    - "classification with joint time frequency scattering"
    - "generative networks as inverse problems with scattering transforms"
    - "diffusion scattering transforms on graphs"
    - "how powerful are graph neural networks"
    - "covariant compositional networks for learning graphs"
    - "geometric deep learning going beyond euclidean data"
    - "graph classification with 2d convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "multi scale geometric summaries for similarity based sensor fusion"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "geometric cross modal comparison of heterogeneous sensor data"
    - "early mfcc and hpcp fusion for robust cover song identification"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-3 (induced by source paper "emergence of invariance and disentanglement in deep representations"):
  with members:
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "sharp minima can generalize for deep nets"
    - "opening the black box of deep neural networks via information"
    - "on the ability of neural nets to express distributions"
    - "deep variational information bottleneck"
    - "understanding deep learning requires rethinking generalization"
    - "entropy sgd biasing gradient descent into wide valleys"

Connected through hyperedge HE-4 (induced by source paper "face recognition using scattering convolutional network"):
  with members:
    - "masked signal decomposition using subspace representation and its applications"
    - "person re identification using visual attention"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "palmprint recognition using deep scattering convolutional network"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "iris recognition using scattering transform and textural features"
    - "fingerprint recognition using translation invariant scattering network"
    - "a bayesian framework for sparse representation based 3 d human pose estimation"

Connected through hyperedge HE-5 (induced by source paper "machine olfaction using time scattering of sensor multiresolution graphs"):
  with members:
    - "unsupervised deep haar scattering on graphs"

Connected through hyperedge HE-6 (induced by source paper "fingerprint recognition using translation invariant scattering network"):
  with members:
    - "screen content image segmentation using sparse smooth decomposition"
    - "iris recognition using scattering transform and textural features"
    - "multi task image classification via collaborative hierarchical spike and slab priors"
    - "screen content image segmentation using least absolute deviation fitting"
    - "multispectral palmprint recognition using textural features"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-7 (induced by source paper "iris recognition using scattering transform and textural features"):
  with members:
    - "screen content image segmentation using least absolute deviation fitting"
    - "multispectral palmprint recognition using textural features"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"
    - "a robust regression approach for background foreground segmentation"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-8 (induced by source paper "deep convolutional neural networks based on semi discrete frames"):
  with members:
    - "deep scattering spectrum"
    - "invariant scattering convolution networks"
    - "representation learning a review and new perspectives"

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)