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: "sparse image representation with epitomes"
  Abstract: Sparse coding, which is the decomposition of a vector using only a few basis elements, is widely used in machine learning and image processing. The basis set, also called dictionary, is learned to adapt to specific data. This approach has proven to be very effective in many image processing tasks. Traditionally, the dictionary is an unstructured “flat” set of atoms. In this paper, we study structured dictionaries [1] which are obtained from an epitome [11], or a set of epitomes. The epitome is itself a small image, and the atoms are all the patches of a chosen size inside this image. This considerably reduces the number of parameters to learn and provides sparse image decompositions with shift-invariance properties. We propose a new formulation and an algorithm for learning the structured dictionaries associated with epitomes, and illustrate their use in image de-noising tasks.

Connected through hyperedge HE-1 (induced by source paper "trainlets dictionary learning in high dimensions"):
  with members:
    - "boosting of image denoising algorithms"
    - "separable dictionary learning"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-2 (induced by source paper "untangling local and global deformations in deep convolutional networks for image classification and sliding window detection"):
  with members:
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "imagenet large scale visual recognition challenge"
    - "deepid net multi stage and deformable deep convolutional neural networks for object detection"
    - "deep epitomic convolutional neural networks"
    - "deformable part models are convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep epitomic convolutional neural networks"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "visualizing and understanding convolutional networks"
    - "stochastic pooling for regularization of deep convolutional neural networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-4 (induced by source paper "separable dictionary learning"):
  with members:
    - "analysis operator learning and its application to image reconstruction"

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)