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: "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
  Abstract: In this paper, we propose an analysis mechanism based structured Analysis Discriminative Dictionary Learning (ADDL) framework. ADDL seamlessly integrates the analysis discriminative dictionary learning, analysis representation and analysis classifier training into a unified model. The applied analysis mechanism can make sure that the learnt dictionaries, representations and linear classifiers over different classes are independent and discriminating as much as possible. The dictionary is obtained by minimizing a reconstruction error and an analytical incoherence promoting term that encourages the sub-dictionaries associated with different classes to be independent. To obtain the representation coefficients, ADDL imposes a sparse l2,1-norm constraint on the coding coefficients instead of using l0 or l1-norm, since the l0 or l1-norm constraint applied in most existing DL criteria makes the training phase time consuming. The codes-extraction projection that bridges data with the sparse codes by extracting special features from the given samples is calculated via minimizing a sparse codes approximation term. Then we compute a linear classifier based on the approximated sparse codes by an analysis mechanism to simultaneously consider the classification and representation powers. Thus, the classification approach of our model is very efficient, because it can avoid the extra time-consuming sparse reconstruction process with trained dictionary for each new test data as most existing DL algorithms. Simulations on real image databases demonstrate that our ADDL model can obtain superior performance over other state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "matrix cofactorization for joint representation learning and supervised classification application to hyperspectral image analysis"):
  with members:
    - "unmixing dynamic pet images with variable specific binding kinetics"
    - "hierarchical bayesian image analysis from low level modeling to robust supervised learning"
    - "task driven dictionary learning for hyperspectral image classification with structured sparsity constraints"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-2 (induced by source paper "soft video multicasting using adaptive compressed sensing"):
  with members:
    - "learning structured twin incoherent twin projective latent dictionary pairs for classification"
    - "scalable block diagonal locality constrained projective dictionary learning"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"

Connected through hyperedge HE-3 (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"
    - "scalable block diagonal locality constrained projective dictionary learning"
    - "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-4 (induced by source paper "learning hybrid representation by robust dictionary learning in factorized compressed space"):
  with members:
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "learning structured twin incoherent twin projective latent dictionary pairs for classification"
    - "discriminative local sparse representation by robust adaptive dictionary pair learning"
    - "joint subspace recovery and enhanced locality driven robust flexible discriminative dictionary learning"
    - "flexible auto weighted local coordinate concept factorization a robust framework for unsupervised clustering"
    - "pointflownet learning representations for rigid motion estimation from point clouds"
    - "fast low rank shared dictionary learning for image classification"

Connected through hyperedge HE-5 (induced by source paper "deep self representative concept factorization network for representation learning"):
  with members:
    - "robust subspace clustering with compressed data"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "the trustworthy pal controlling the false discovery rate in boolean matrix factorization"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "flexible auto weighted local coordinate concept factorization a robust framework for unsupervised clustering"
    - "robust unsupervised flexible auto weighted local coordinate concept factorization for image clustering"
    - "did distributed incremental block coordinate descent for nonnegative matrix factorization"
    - "latitude a model for mixed linear tropical matrix factorization"

Connected through hyperedge HE-6 (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"
    - "scalable block diagonal locality constrained projective dictionary learning"
    - "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-7 (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"
    - "scalable block diagonal locality constrained projective dictionary learning"
    - "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-8 (induced by source paper "locality constraint dictionary learning with support vector for pattern classification"):
  with members:
    - "scalable block diagonal locality constrained projective dictionary learning"
    - "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"

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)