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 novel set of rotationally and translationally invariant features for images based on the non commutative bispectrum"
  Abstract: We propose a new set of rotationally and translationally invariant features for image or pattern recognition and classification. The new features are cubic polynomials in the pixel intensities and provide a richer representation of the original image than most existing systems of invariants. Our construction is based on the generalization of the concept of bispectrum to the three-dimensional rotation group SO(3), and a projection of the image onto the sphere.

Connected through hyperedge HE-1 (induced by source paper "unsupervised co learning on mathcal g manifolds across irreducible representations"):
  with members:
    - "cryo electron microscopy image analysis using multi frequency vector diffusion maps"
    - "a survey of multi view representation learning"
    - "multi frequency phase synchronization"
    - "multi frequency vector diffusion maps"
    - "a study of the classification of low dimensional data with supervised manifold learning"
    - "fast steerable principal component analysis"
    - "a survey on multi view learning"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "bispectrum inversion with application to multireference alignment"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "on signal reconstruction from frog measurements"
    - "fourier phase retrieval uniqueness and algorithms"
    - "on the uniqueness of frog methods"
    - "the sample complexity of multi reference alignment"
    - "non convex phase retrieval from stft measurements"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "fundamental limits in multi image alignment"

Connected through hyperedge HE-3 (induced by source paper "group equivariant convolutional networks"):
  with members:
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "identity mappings in deep residual networks"
    - "wide residual networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning to see by moving"
    - "on invariance and selectivity in representation learning"

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)