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: "trainlets dictionary learning in high dimensions"
  Abstract: Sparse representation has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance. Combined with the ability to learn a dictionary from signal examples, sparsity-inspired algorithms are often achieving state-of-the-art results in a wide variety of tasks. These methods have traditionally been restricted to small dimensions mainly due to the computational constraints that the dictionary learning problem entails. In the context of image processing, this implies handling small image patches. In this work we show how to efficiently handle bigger dimensions and go beyond the small patches in sparsity-based signal and image processing methods. We build our approach based on a new cropped Wavelet decomposition, which enables a multi-scale analysis with virtually no border effects. We then employ this as the base dictionary within a double sparsity model to enable the training of adaptive dictionaries. To cope with the increase of training data, while at the same time improving the training performance, we present an Online Sparse Dictionary Learning (OSDL) algorithm to train this model effectively, enabling it to handle millions of examples. This work shows that dictionary learning can be up-scaled to tackle a new level of signal dimensions, obtaining large adaptable atoms that we call Trainlets.

Connected through hyperedge HE-1 (induced by source paper "dictionary learning for adaptive gpr landmine classification"):
  with members:
    - "sub nyquist radar principles and prototypes"
    - "on learning with shift invariant structures"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "fast low rank shared dictionary learning for image classification"
    - "performance of time delay estimation in a cognitive radar"
    - "online dictionary learning aided target recognition in cognitive gpr"
    - "cognitive sub nyquist hardware prototype of a collocated mimo radar"
    - "fast r cnn"

Connected through hyperedge HE-2 (induced by source paper "multilayer convolutional sparse modeling pursuit and dictionary learning"):
  with members:
    - "convolutional dictionary learning via local processing"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "online convolutional dictionary learning"
    - "maximal sparsity with deep networks"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "on the expressive power of deep learning a tensor analysis"
    - "winner take all autoencoders"
    - "k sparse autoencoders"

Connected through hyperedge HE-3 (induced by source paper "on multi layer basis pursuit efficient algorithms and convolutional neural networks"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "deep component analysis via alternating direction neural networks"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "densely connected convolutional networks"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "sparse recovery with coherent tight frames via analysis dantzig selector and analysis lasso"

Connected through hyperedge HE-4 (induced by source paper "approximate fast graph fourier transforms via multilayer sparse approximations"):
  with members:
    - "fast orthonormal sparsifying transforms based on householder reflectors"
    - "compressive spectral clustering"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "gspbox a toolbox for signal processing on graphs"
    - "powers of tensors and fast matrix multiplication"
    - "discrete signal processing on graphs frequency analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-5 (induced by source paper "stable safe screening and structured dictionaries for faster ell _ 1 regularization"):
  with members:
    - "screening tests for lasso problems"
    - "joint screening tests for lasso"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "separable dictionary learning"
    - "safe screening with variational inequalities and its application to lasso"
    - "lasso screening rules via dual polytope projection"
    - "safe feature elimination in sparse supervised learning"

Connected through hyperedge HE-6 (induced by source paper "a local block coordinate descent algorithm for the convolutional sparse coding model"):
  with members:
    - "multi focus image fusion via coupled sparse representation and dictionary learning"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "scalable online convolutional sparse coding"
    - "online convolutional dictionary learning"
    - "an overview of gradient descent optimization algorithms"
    - "sparse modeling for image and vision processing"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-7 (induced by source paper "online learning sensing matrix and sparsifying dictionary simultaneously for compressive sensing"):
  with members:
    - "on collaborative compressive sensing systems the framework design and algorithm"
    - "global optimality in low rank matrix optimization"
    - "seboost boosting stochastic learning using subspace optimization techniques"
    - "an efficient method for robust projection matrix design"
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"
    - "projection design for statistical compressive sensing a tight frame based approach"

Connected through hyperedge HE-8 (induced by source paper "learning fast sparsifying transforms"):
  with members:
    - "fast orthonormal sparsifying transforms based on householder reflectors"
    - "on the computational intractability of exact and approximate dictionary learning"
    - "fast approximation of rotations and hessians matrices"
    - "message passing algorithms for 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)