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: "kernel spectral curvature clustering kscc"
  Abstract: Multi-manifold modeling is increasingly used in segmentation and data representation tasks in computer vision and related fields. While the general problem, modeling data by mixtures of manifolds, is very challenging, several approaches exist for modeling data by mixtures of affine subspaces (which is often referred to as hybrid linear modeling). We translate some important instances of multi-manifold modeling to hybrid linear modeling in embedded spaces, without explicitly performing the embedding but applying the kernel trick. The resulting algorithm, Kernel Spectral Curvature Clustering, uses kernels at two levels - both as an implicit embedding method to linearize non-flat manifolds and as a principled method to convert a multi-way affinity problem into a spectral clustering one. We demonstrate the effectiveness of the method by comparing it with other state-of-the-art methods on both synthetic data and a real-world problem of segmenting multiple motions from two perspective camera views.

Connected through hyperedge HE-1 (induced by source paper "neural collaborative subspace clustering"):
  with members:
    - "scalable deep k subspace clustering"
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep subspace clustering networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "adaptive low rank kernel subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-2 (induced by source paper "deep subspace clustering networks"):
  with members:
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "unsupervised deep embedding for clustering analysis"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "adam a method for stochastic optimization"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-3 (induced by source paper "a new approach to two view motion segmentation using global dimension minimization"):
  with members:
    - "robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "hybrid linear modeling via local best fit flats"
    - "a geometric analysis of subspace clustering with outliers"
    - "randomized hybrid linear modeling by local best fit flats"
    - "median k flats for hybrid linear modeling with many outliers"

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)