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: "shape interaction matrix revisited and robustified efficient subspace clustering with corrupted and incomplete data"
  Abstract: The Shape Interaction Matrix (SIM) is one of the earliest approaches to performing subspace clustering (i.e., separating points drawn from a union of subspaces). In this paper, we revisit the SIM and reveal its connections to several recent subspace clustering methods. Our analysis lets us derive a simple, yet effective algorithm to robustify the SIM and make it applicable to realistic scenarios where the data is corrupted by noise. We justify our method by intuitive examples and the matrix perturbation theory. We then show how this approach can be extended to handle missing data, thus yielding an efficient and general subspace clustering algorithm. We demonstrate the benefits of our approach over state-of-the-art subspace clustering methods on several challenging motion segmentation and face clustering problems, where the data includes corrupted and missing measurements.

Connected through hyperedge HE-1 (induced by source paper "multi level representation learning for deep subspace clustering"):
  with members:
    - "self supervised convolutional subspace clustering network"
    - "deep spectral clustering using dual autoencoder network"
    - "neural collaborative subspace clustering"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep multimodal subspace clustering networks"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deep subspace clustering networks"

Connected through hyperedge HE-2 (induced by source paper "learning for multi type subspace clustering"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "deep subspace clustering networks"
    - "convolutional neural network architecture for geometric matching"
    - "multi class model fitting by energy minimization and mode seeking"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "relative camera pose estimation using convolutional neural networks"
    - "learning to find good correspondences"
    - "nonnegative matrix underapproximation for robust multiple model fitting"

Connected through hyperedge HE-3 (induced by source paper "learning for multi model and multi type fitting"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "deep subspace clustering networks"
    - "convolutional neural network architecture for geometric matching"
    - "multi class model fitting by energy minimization and mode seeking"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "relative camera pose estimation using convolutional neural networks"
    - "learning to find good correspondences"
    - "deep image homography estimation"

Connected through hyperedge HE-4 (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-5 (induced by source paper "robust motion segmentation from pairwise matches"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "multi class model fitting by energy minimization and mode seeking"
    - "a survey of structure from motion"
    - "spectral synchronization of multiple views in se 3"
    - "on constrained spectral clustering and its applications"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-6 (induced by source paper "scaled simplex representation for subspace clustering"):
  with members:
    - "ell_0 motivated low rank sparse subspace clustering"
    - "robust graph learning from noisy data"
    - "low rank kernel learning for graph based clustering"
    - "discriminative regression machine a classifier for high dimensional data or imbalanced data"
    - "clustering with similarity preserving"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "discriminative transformation learning for fuzzy sparse subspace clustering"
    - "external prior guided internal prior learning for real world noisy image denoising"

Connected through hyperedge HE-7 (induced by source paper "scalable deep k subspace clustering"):
  with members:
    - "deep sparse subspace clustering"
    - "deep subspace clustering networks"
    - "non linear subspace clustering with learned low rank kernels"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "robust multi body feature tracker a segmentation free approach"
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-8 (induced by source paper "motion segmentation by exploiting complementary geometric models"):
  with members:
    - "on constrained spectral clustering and its applications"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "a tutorial on spectral clustering"

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)