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: "learning locality constrained collaborative representation for robust face recognition"
  Abstract: Abstract   The models of low-dimensional manifold and sparse representation are two well-known concise models that suggest that each data can be described by a few characteristics. Manifold learning is usually investigated for dimension reduction by preserving some expected local geometric structures from the original space into a low-dimensional one. The structures are generally determined by using pairwise distance, e.g., Euclidean distance. Alternatively, sparse representation denotes a data point as a linear combination of the points from the same subspace. In practical applications, however, the nearby points in terms of pairwise distance may not belong to the same subspace, and vice versa. Consequently, it is interesting and important to explore how to get a better representation by integrating these two models together. To this end, this paper proposes a novel coding algorithm, called Locality-Constrained Collaborative Representation (LCCR), which introduce a kind of local consistency into coding scheme to improve the discrimination of the representation. The locality term derives from a biologic observation that the similar inputs have similar codes. The objective function of LCCR has an analytical solution, and it does not involve local minima. The empirical studies based on several popular facial databases show that LCCR is promising in recognizing human faces with varying pose, expression and illumination, as well as various corruptions and occlusions.

Connected through hyperedge HE-1 (induced by source paper "collaboratively weighting deep and classic representation via l2 regularization for image classification"):
  with members:
    - "on the decision boundary of deep neural networks"
    - "random erasing data augmentation"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning structured sparsity in deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "wide residual networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "connections between nuclear norm and frobenius norm based representations"):
  with members:
    - "automatic subspace learning via principal coefficients embedding"
    - "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"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "collaborative representation based classification for face recognition"
    - "sparse subspace clustering algorithm theory and applications"
    - "learning efficient sparse and low rank models"

Connected through hyperedge HE-3 (induced by source paper "constructing the l2 graph for robust subspace learning and subspace clustering"):
  with members:
    - "robust subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "low rank structure learning via nonconvex heuristic recovery"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "fixed rank representation for unsupervised visual learning"
    - "sparse subspace clustering algorithm theory and applications"
    - "matrix completion with noise"

Connected through hyperedge HE-4 (induced by source paper "sparse coding with earth mover s distance for multi instance histogram representation"):
  with members:
    - "representing data by sparse combination of contextual data points for classification"
    - "a novel multivariate performance optimization method based on sparse coding and hyper predictor learning"
    - "multi instance learning by treating instances as non i i d samples"

Connected through hyperedge HE-5 (induced by source paper "fast low rank representation based spatial pyramid matching for image classification"):
  with members:
    - "connections between nuclear norm and frobenius norm based representations"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-6 (induced by source paper "an out of sample extension of sparse subspace clustering and low rank representation for clustering large scale data sets"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "fixed rank representation for unsupervised visual learning"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

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)