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: "subspace clustering by block diagonal representation"
  Abstract: This paper studies the subspace clustering problem. Given some data points approximately drawn from a union of subspaces, the goal is to group these data points into their underlying subspaces. Many subspace clustering methods have been proposed and among which sparse subspace clustering and low-rank representation are two representative ones. Despite the different motivations, we observe that many existing methods own the common block diagonal property, which possibly leads to correct clustering, yet with their proofs given case by case. In this work, we consider a general formulation and provide a unified theoretical guarantee of the block diagonal property. The block diagonal property of many existing methods falls into our special case. Second, we observe that many existing methods approximate the block diagonal representation matrix by using different structure priors, e.g., sparsity and low-rankness, which are indirect. We propose the first block diagonal matrix induced regularizer for directly pursuing the block diagonal matrix. With this regularizer, we solve the subspace clustering problem by Block Diagonal Representation (BDR), which uses the block diagonal structure prior. The BDR model is nonconvex and we propose an alternating minimization solver and prove its convergence. Experiments on real datasets demonstrate the effectiveness of BDR.

Connected through hyperedge HE-1 (induced by source paper "probabilistic k means clustering via nonlinear programming"):
  with members:
    - "clustering millions of faces by identity"
    - "an information theoretic analysis of hard and soft assignment methods for clustering"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "multiple flat projections for cross manifold clustering"):
  with members:
    - "a general model for plane based clustering with loss function"
    - "insensitive stochastic gradient twin support vector machines for large scale problems"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "robust subspace clustering with compressed data"):
  with members:
    - "automatic subspace learning via principal coefficients embedding"
    - "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "robust subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"

Connected through hyperedge HE-4 (induced by source paper "neither global nor local a hierarchical robust subspace clustering for image data"):
  with members:
    - "robust subspace clustering by cauchy loss function"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "robust and efficient subspace segmentation via least squares regression"
    - "learning transformations for clustering and classification"
    - "robust recovery of subspace structures by low rank representation"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"
    - "generalized principal component analysis gpca"

Connected through hyperedge HE-5 (induced by source paper "multi view deep subspace clustering networks"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "deep multimodal subspace clustering networks"
    - "simple to complex cross modal learning to rank"
    - "non local neural networks"
    - "deep subspace clustering networks"
    - "deconvolutional paragraph representation learning"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "multi view 3d object detection network for autonomous driving"

Connected through hyperedge HE-6 (induced by source paper "multilayer collaborative low rank coding network for robust deep subspace discovery"):
  with members:
    - "robust graph learning from noisy data"
    - "robust subspace clustering with compressed data"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "robust triple matrix recovery based auto weighted label propagation for classification"
    - "discriminative local sparse representation by robust adaptive dictionary pair learning"
    - "adaptive structure constrained robust latent low rank coding for image recovery"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (induced by source paper "three stage subspace clustering framework with graph based transformation and optimization"):
  with members:
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "greedy feature selection for subspace clustering"

Connected through hyperedge HE-8 (induced by source paper "minimal sample subspace learning theory and algorithms"):
  with members:
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "innovation pursuit a new approach to subspace clustering"
    - "robust subspace clustering via smoothed rank approximation"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "comparing measures of sparsity"

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)