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 transformations for clustering and classification"
  Abstract: A low-rank transformation learning framework for subspace clustering and classification is here proposed. Many high-dimensional data, such as face images and motion sequences, approximately lie in a union of low-dimensional subspaces. The corresponding subspace clustering problem has been extensively studied in the literature to partition such high-dimensional data into clusters corresponding to their underlying low-dimensional subspaces. However, low-dimensional intrinsic structures are often violated for real-world observations, as they can be corrupted by errors or deviate from ideal models. We propose to address this by learning a linear transformation on subspaces using matrix rank, via its convex surrogate nuclear norm, as the optimization criteria. The learned linear transformation restores a low-rank structure for data from the same subspace, and, at the same time, forces a a maximally separated structure for data from different subspaces. In this way, we reduce variations within subspaces, and increase separation between subspaces for a more robust subspace clustering. This proposed learned robust subspace clustering framework significantly enhances the performance of existing subspace clustering methods. Basic theoretical results here presented help to further support the underlying framework. To exploit the low-rank structures of the transformed subspaces, we further introduce a fast subspace clustering technique, which efficiently combines robust PCA with sparse modeling. When class labels are present at the training stage, we show this low-rank transformation framework also significantly enhances classification performance. Extensive experiments using public datasets are presented, showing that the proposed approach significantly outperforms state-of-the-art methods for subspace clustering and classification.

Connected through hyperedge HE-1 (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"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "robust and efficient subspace segmentation via least squares regression"
    - "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-2 (induced by source paper "artistic domain generalisation methods are limited by their deep representations"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning to generalize meta learning for domain generalization"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "deeper broader and artier domain generalization"
    - "generalisation and sharing in triplet convnets for sketch based visual search"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "domain separation networks"
    - "domain generalization for object recognition with multi task autoencoders"

Connected through hyperedge HE-3 (induced by source paper "discriminative transformation learning for fuzzy sparse subspace clustering"):
  with members:
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "discriminative nonlinear analysis operator learning when cosparse model meets image classification"
    - "joint sparse recovery with semisupervised music"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "compressive spectral clustering"
    - "robust subspace clustering via smoothed rank approximation"
    - "robust subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"

Connected through hyperedge HE-4 (induced by source paper "foresthash semantic hashing with shallow random forests and tiny convolutional networks"):
  with members:
    - "ol e orthogonal low rank embedding a plug and play geometric loss for deep learning"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "striving for simplicity the all convolutional net"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "learning transformations for classification forests"

Connected through hyperedge HE-5 (induced by source paper "deep neural networks with random gaussian weights a universal classification strategy"):
  with members:
    - "deep learning in neural networks"
    - "geometry aware deep transform"
    - "understanding deep image representations by inverting them"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "the loss surfaces of multilayer networks"
    - "convolutional kernel networks"

Connected through hyperedge HE-6 (induced by source paper "not afraid of the dark nir vis face recognition via cross spectral hallucination and low rank embedding"):
  with members:
    - "accelerating the super resolution convolutional neural network"
    - "colorful image colorization"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep perceptual mapping for thermal to visible face recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "return of the devil in the details delving deep into convolutional nets"
    - "shared representation learning for heterogeneous face recognition"

Connected through hyperedge HE-7 (induced by source paper "bounds on the number of measurements for reliable compressive classification"):
  with members:
    - "mismatch in the classification of linear subspaces sufficient conditions for reliable classification"
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "sparse subspace clustering algorithm theory and applications"
    - "online adaptive statistical compressed sensing of gaussian mixture models"
    - "model based compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "mismatch in the classification of linear subspaces sufficient conditions for reliable classification"):
  with members:
    - "compressive classification of a mixture of gaussians analysis designs and geometrical interpretation"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "sparse subspace clustering algorithm theory and applications"
    - "general deviants an analysis of perturbations in compressed sensing"

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)