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: "smoothing proximal gradient method for general structured sparse learning"
  Abstract: We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input or output sides. We consider two widely adopted types of such penalties as our motivating examples: 1) overlapping group lasso penalty, based on the l1/l2 mixed-norm penalty, and 2) graph-guided fusion penalty. For both types of penalties, due to their non-separability, developing an efficient optimization method has remained a challenging problem. In this paper, we propose a general optimization approach, called smoothing proximal gradient method, which can solve the structured sparse regression problems with a smooth convex loss and a wide spectrum of structured-sparsity-inducing penalties. Our approach is based on a general smoothing technique of Nesterov. It achieves a convergence rate faster than the standard first-order method, subgradient method, and is much more scalable than the most widely used interior-point method. Numerical results are reported to demonstrate the efficiency and scalability of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "learning from weak and noisy labels for semantic segmentation"):
  with members:
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "joint calibration for semantic segmentation"
    - "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "simultaneous detection and segmentation"
    - "fully convolutional multi class multiple instance learning"
    - "from image level to pixel level labeling with convolutional networks"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "multi modal face pose estimation with multi task manifold deep learning"):
  with members:
    - "funnel structured cascade for multi view face detection with alignment awareness"
    - "deepsaliency multi task deep neural network model for salient object detection"

Connected through hyperedge HE-3 (induced by source paper "image classification by visual bag of words refinement and reduction"):
  with members:
    - "exhaustive and efficient constraint propagation a graph based learning approach and its applications"

Connected through hyperedge HE-4 (induced by source paper "multi task cnn model for attribute prediction"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "zero shot recognition with unreliable attributes"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "visualizing and understanding convolutional networks"
    - "deep convolutional ranking for multilabel image annotation"

Connected through hyperedge HE-5 (induced by source paper "semantic sparse recoding of visual content for image applications"):
  with members:
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-6 (induced by source paper "pairwise constraint propagation on multi view data"):
  with members:
    - "semantic sparse recoding of visual content for image applications"

Connected through hyperedge HE-7 (induced by source paper "optimal computational trade off of inexact proximal methods"):
  with members:
    - "a general framework for structured sparsity via proximal optimization"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-8 (induced by source paper "convex multitask learning with flexible task clusters"):
  with members:
    - "a convex formulation for learning task relationships in multi task learning"
    - "clustered multi task learning a convex formulation"

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)