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: "optimization methods for convolutional sparse coding"
  Abstract: Sparse and convolutional constraints form a natural prior for many optimization problems that arise from physical processes. Detecting motifs in speech and musical passages, super-resolving images, compressing videos, and reconstructing harmonic motions can all leverage redundancies introduced by convolution. Solving problems involving sparse and convolutional constraints remains a difficult computational problem, however. In this paper we present an overview of convolutional sparse coding in a consistent framework. The objective involves iteratively optimizing a convolutional least-squares term for the basis functions, followed by an L1-regularized least squares term for the sparse coefficients. We discuss a range of optimization methods for solving the convolutional sparse coding objective, and the properties that make each method suitable for different applications. In particular, we concentrate on computational complexity, speed to {\epsilon} convergence, memory usage, and the effect of implied boundary conditions. We present a broad suite of examples covering different signal and application domains to illustrate the general applicability of convolutional sparse coding, and the efficacy of the available optimization methods.

Connected through hyperedge HE-1 (induced by source paper "on learning with shift invariant structures"):
  with members:
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "dictionary learning for adaptive gpr target classification"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "compressive shift retrieval"
    - "shift invariance sparse coding for audio classification"
    - "circulant and toeplitz matrices in compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "convolutional dictionary learning acceleration and convergence"):
  with members:
    - "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"
    - "convolutional sparse coding for high dynamic range imaging"
    - "working locally thinking globally part ii stability and algorithms for convolutional sparse coding"
    - "a fast patch dictionary method for whole image recovery"
    - "sparse modeling for image and vision processing"
    - "fast stochastic alternating direction method of multipliers"

Connected through hyperedge HE-3 (induced by source paper "convolutional sparse coding for capturing high speed video content"):
  with members:
    - "convolutional sparse coding for high dynamic range imaging"
    - "convolutional matching pursuit and dictionary training"
    - "compressed compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "supervised convolutional sparse coding"):
  with members:
    - "cross label suppression a discriminative and fast dictionary learning with group regularization"
    - "dictionary learning for deblurring and digital zoom"

Connected through hyperedge HE-5 (induced by source paper "working locally thinking globally theoretical guarantees for convolutional sparse coding"):
  with members:
    - "on the global local dichotomy in sparsity modeling"
    - "convolutional dictionary learning through tensor factorization"
    - "boosting of image denoising algorithms"
    - "sparse modeling for image and vision processing"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "convolutional matching pursuit and dictionary training"

Connected through hyperedge HE-6 (induced by source paper "fast convolutional sparse coding in the dual domain"):
  with members:
    - "fast stochastic alternating direction method of multipliers"
    - "dictionary learning for deblurring and digital zoom"

Connected through hyperedge HE-7 (induced by source paper "discovery of latent factors in high dimensional data using tensor methods"):
  with members:
    - "scalable latent tree model and its application to health analytics"
    - "skip thought vectors"
    - "self adaptive hierarchical sentence model"
    - "convolutional dictionary learning through tensor factorization"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards universal paraphrastic sentence embeddings"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-8 (induced by source paper "unsupervised learning of word sequence representations from scratch via convolutional tensor decomposition"):
  with members:
    - "skip thought vectors"
    - "self adaptive hierarchical sentence model"
    - "natural language inference by tree based convolution and heuristic matching"
    - "convolutional dictionary learning through tensor factorization"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards universal paraphrastic sentence embeddings"
    - "distributed representations of sentences and documents"
    - "effective use of word order for text categorization with convolutional neural networks"

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)