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: "correlation adaptive subspace segmentation by trace lasso"
  Abstract: This paper studies the subspace segmentation problem. Given a set of data points drawn from a union of subspaces, the goal is to partition them into their underlying subspaces they were drawn from. The spectral clustering method is used as the framework. It requires to find an affinity matrix which is close to block diagonal, with nonzero entries corresponding to the data point pairs from the same subspace. In this work, we argue that both sparsity and the grouping effect are important for subspace segmentation. A sparse affinity matrix tends to be block diagonal, with less connections between data points from different subspaces. The grouping effect ensures that the highly corrected data which are usually from the same subspace can be grouped together. Sparse Subspace Clustering (SSC), by using $\ell^1$-minimization, encourages sparsity for data selection, but it lacks of the grouping effect. On the contrary, Low-Rank Representation (LRR), by rank minimization, and Least Squares Regression (LSR), by $\ell^2$-regularization, exhibit strong grouping effect, but they are short in subset selection. Thus the obtained affinity matrix is usually very sparse by SSC, yet very dense by LRR and LSR. #R##N#In this work, we propose the Correlation Adaptive Subspace Segmentation (CASS) method by using trace Lasso. CASS is a data correlation dependent method which simultaneously performs automatic data selection and groups correlated data together. It can be regarded as a method which adaptively balances SSC and LSR. Both theoretical and experimental results show the effectiveness of CASS.

Connected through hyperedge HE-1 (induced by source paper "self supervised convolutional subspace clustering network"):
  with members:
    - "constrained sparse subspace clustering with side information"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "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"

Connected through hyperedge HE-2 (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"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "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-3 (induced by source paper "robust subspace clustering by cauchy loss function"):
  with members:
    - "multi view intact space learning"
    - "sce a manifold regularized set covering method for data partitioning"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "a unified framework for representation based subspace clustering of out of sample and large scale data"
    - "joint unsupervised learning of deep representations and image clusters"
    - "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-4 (induced by source paper "simultaneous subspace clustering and cluster number estimating based on triplet relationship"):
  with members:
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "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"
    - "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-5 (induced by source paper "a multi view dimensionality reduction algorithm based on smooth representation model"):
  with members:
    - "multi view intact space learning"
    - "a survey of multi view representation learning"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "lstm assisted evolutionary self expressive subspace clustering"):
  with members:
    - "theoretical analysis of sparse subspace clustering with missing entries"
    - "evolutionary self expressive models for subspace clustering"
    - "accelerated sparse subspace clustering"
    - "consensus of hybrid multi agent systems"
    - "adaptive evolutionary clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "greedy feature selection for subspace clustering"

Connected through hyperedge HE-7 (induced by source paper "research on clustering performance of sparse subspace clustering"):
  with members:
    - "subspace clustering using a symmetric low rank representation"
    - "symmetric low rank representation for subspace clustering"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "clustering with jointly learned nonlinear transforms over discriminating min max similarity dissimilarity assignment"):
  with members:
    - "non negative matrix factorization with sparseness constraints"

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)