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: "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
  Abstract: Subspace clustering refers to the problem of segmenting data drawn from a union of subspaces. State-of-the-art approaches for solving this problem follow a two-stage approach. In the first step, an affinity matrix is learned from the data using sparse or low-rank minimization techniques. In the second step, the segmentation is found by applying spectral clustering to this affinity. While this approach has led to the state-of-the-art results in many applications, it is suboptimal, because it does not exploit the fact that the affinity and the segmentation depend on each other. In this paper, we propose a joint optimization framework — Structured Sparse Subspace Clustering (S3C) — for learning both the affinity and the segmentation. The proposed S3C framework is based on expressing each data point as a structured sparse linear combination of all other data points, where the structure is induced by a norm that depends on the unknown segmentation. Moreover, we extend the proposed S3C framework into Constrained S3C (CS3C) in which available partial side-information is incorporated into the stage of learning the affinity. We show that both the structured sparse representation and the segmentation can be found via a combination of an alternating direction method of multipliers with spectral clustering. Experiments on a synthetic data set, the Extended Yale B face data set, the Hopkins 155 motion segmentation database, and three cancer data sets demonstrate the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "self expressive subspace clustering to recognize motion dynamics of a multi joint coordination for chronic ankle instability"):
  with members:
    - "self expressive decompositions for matrix approximation and clustering"
    - "sparse subspace clustering algorithm theory and applications"
    - "clustering partially observed graphs via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "self supervised convolutional subspace clustering network"):
  with members:
    - "constrained sparse subspace clustering with side information"
    - "deep sparse subspace clustering"
    - "deep subspace clustering networks"
    - "non linear subspace clustering with learned low rank kernels"
    - "ol e orthogonal low rank embedding a plug and play geometric loss for deep learning"
    - "adaptive low rank kernel subspace clustering"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"

Connected through hyperedge HE-3 (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"
    - "subspace clustering by block diagonal representation"
    - "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-4 (induced by source paper "clustering aware graph construction a joint learning perspective"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "graph learning from data under laplacian and structural constraints"
    - "learning sparse graphs under smoothness prior"
    - "robust principal component analysis on graphs"
    - "learning laplacian matrix in smooth graph signal representations"
    - "robust and efficient subspace segmentation via least squares regression"

Connected through hyperedge HE-5 (induced by source paper "residual encoder decoder network for deep subspace clustering"):
  with members:
    - "sparse dense subspace clustering"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
    - "deep subspace clustering networks"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "training very deep networks"

Connected through hyperedge HE-6 (induced by source paper "simultaneous subspace clustering and cluster number estimating based on triplet relationship"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "facenet a unified embedding for face recognition and clustering"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "restricted connection orthogonal matching pursuit for sparse subspace clustering"):
  with members:
    - "robust subspace clustering by cauchy loss function"
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "noisy subspace clustering via matching pursuits"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "algebraic clustering of affine subspaces"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-8 (induced by source paper "three stage subspace clustering framework with graph based transformation and optimization"):
  with members:
    - "subspace clustering by block diagonal representation"
    - "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"

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)