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: "trace lasso a trace norm regularization for correlated designs"
  Abstract: Using the $\ell_1$-norm to regularize the estimation of the parameter vector of a linear model leads to an unstable estimator when covariates are highly correlated. In this paper, we introduce a new penalty function which takes into account the correlation of the design matrix to stabilize the estimation. This norm, called the trace Lasso, uses the trace norm, which is a convex surrogate of the rank, of the selected covariates as the criterion of model complexity. We analyze the properties of our norm, describe an optimization algorithm based on reweighted least-squares, and illustrate the behavior of this norm on synthetic data, showing that it is more adapted to strong correlations than competing methods such as the elastic net.

Connected through hyperedge HE-1 (induced by source paper "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"):
  with members:
    - "connections between nuclear norm and frobenius norm based representations"
    - "noisy subspace clustering via matching pursuits"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "sssc am a unified framework for video co segmentation by structured sparse subspace clustering with appearance and motion features"
    - "convex sparse spectral clustering single view to multi view"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "correlation adaptive subspace segmentation by trace lasso"

Connected through hyperedge HE-2 (induced by source paper "cross modal subspace learning for fine grained sketch based image retrieval"):
  with members:
    - "a comprehensive survey on cross modal retrieval"
    - "compound rank k projections for bilinear analysis"
    - "free hand sketch synthesis with deformable stroke models"
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "multimodal sparse coding for event detection"
    - "sketch a net that beats humans"
    - "a convex formulation for spectral shrunk clustering"

Connected through hyperedge HE-3 (induced by source paper "robust matrix elastic net based canonical correlation analysis an effective algorithm for multi view unsupervised learning"):
  with members:
    - "constrained low rank learning using least squares based regularization"
    - "unsupervised learning of long term motion dynamics for videos"
    - "a comprehensive survey on cross modal retrieval"
    - "efficient globally convergent stochastic optimization for canonical correlation analysis"
    - "large scale approximate kernel canonical correlation analysis"
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "a survey on multi view learning"

Connected through hyperedge HE-4 (induced by source paper "coupled deep learning for heterogeneous face recognition"):
  with members:
    - "viplfacenet an open source deep face recognition sdk"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "a lightened cnn for deep face representation"
    - "facenet a unified embedding for face recognition and clustering"
    - "shared representation learning for heterogeneous face recognition"

Connected through hyperedge HE-5 (induced by source paper "color face recognition using high dimension quaternion based adaptive representation"):
  with members:
    - "robust face recognition via adaptive sparse representation"
    - "robust principal component analysis"

Connected through hyperedge HE-6 (induced by source paper "optimization on submanifolds of convolution kernels in cnns"):
  with members:
    - "deep networks with stochastic depth"
    - "densely connected convolutional networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "all you need is a good init"
    - "trading accuracy for numerical stability orthogonalization biorthogonalization and regularization"
    - "symmetry invariant optimization in deep networks"
    - "unitary evolution recurrent neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "oracle based active set algorithm for scalable elastic net subspace clustering"):
  with members:
    - "filtrated spectral algebraic subspace 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"
    - "greedy feature selection for subspace clustering"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-8 (induced by source paper "on clustering and embedding manifolds using a low rank neighborhood approach"):
  with members:
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "robust multiple manifolds structure learning"
    - "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.LG(Machine Learning)