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: "scalable block diagonal locality constrained projective dictionary learning"
  Abstract: We propose a novel structured discriminative block-diagonal dictionary learning method, referred to as scalable Locality-Constrained Projective Dictionary Learning (LC-PDL), for efficient representation and classification. To improve the scalability by saving both training and testing time, our LC-PDL aims at learning a structured discriminative dictionary and a block-diagonal representation without using costly l0/l1-norm. Besides, it avoids extra time-consuming sparse reconstruction process with the well-trained dictionary for new sample as many existing models. More importantly, LC-PDL avoids using the complementary data matrix to learn the sub-dictionary over each class. To enhance the performance, we incorporate a locality constraint of atoms into the DL procedures to keep local information and obtain the codes of samples over each class separately. A block-diagonal discriminative approximation term is also derived to learn a discriminative projection to bridge data with their codes by extracting the special block-diagonal features from data, which can ensure the approximate coefficients to associate with its label information clearly. Then, a robust multiclass classifier is trained over extracted block-diagonal codes for accurate label predictions. Experimental results verify the effectiveness of our algorithm.

Connected through hyperedge HE-1 (induced by source paper "soft video multicasting using adaptive compressed sensing"):
  with members:
    - "learning structured twin incoherent twin projective latent dictionary pairs for classification"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"

Connected through hyperedge HE-2 (induced by source paper "convolutional dictionary pair learning network for image representation learning"):
  with members:
    - "majorization minimization technique for optimally solving deep dictionary learning"
    - "multilayer collaborative low rank coding network for robust deep subspace discovery"
    - "robust subspace clustering with compressed data"
    - "learning structured twin incoherent twin projective latent dictionary pairs for classification"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "discriminative local sparse representation by robust adaptive dictionary pair learning"
    - "joint subspace recovery and enhanced locality driven robust flexible discriminative dictionary learning"
    - "deep micro dictionary learning and coding network"

Connected through hyperedge HE-3 (induced by source paper "discriminative local sparse representation by robust adaptive dictionary pair learning"):
  with members:
    - "robust subspace clustering with compressed data"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "learning structured twin incoherent twin projective latent dictionary pairs for classification"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "joint subspace recovery and enhanced locality driven robust flexible discriminative dictionary learning"
    - "kernel induced label propagation by mapping for semi supervised classification"
    - "cnn based joint clustering and representation learning with feature drift compensation for large scale image data"
    - "fast low rank shared dictionary learning for image classification"

Connected through hyperedge HE-4 (induced by source paper "robust subspace discovery by block diagonal adaptive locality constrained representation"):
  with members:
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "robust unsupervised flexible auto weighted local coordinate concept factorization for image clustering"
    - "nonlocal low rank tensor factor analysis for image restoration"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "subspace clustering by block diagonal representation"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-5 (induced by source paper "locality constraint dictionary learning with support vector for pattern classification"):
  with members:
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "label embedded dictionary learning for image classification"
    - "analysis dictionary learning based classification structure for robustness"
    - "multi focus image fusion using dictionary learning and low rank representation"
    - "cross label suppression a discriminative and fast dictionary learning with group regularization"
    - "fast low rank shared dictionary learning for image classification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "learning structured twin incoherent twin projective latent dictionary pairs for classification"):
  with members:
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "joint subspace recovery and enhanced locality driven robust flexible discriminative dictionary learning"
    - "fast low rank shared dictionary learning for image classification"
    - "self expressive dictionary learning for dynamic 3d 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.CV(Computer Vision and Pattern Recognition)