human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep micro dictionary learning and coding network"):
  Abstract: In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding Network (DDLCN). DDLCN has most of the standard deep learning layers (pooling, fully, connected, input/output, etc.) but the main difference is that the fundamental convolutional layers are replaced by novel compound dictionary learning and coding layers. The dictionary learning layer learns an over-complete dictionary for the input training data. At the deep coding layer, a locality constraint is added to guarantee that the activated dictionary bases are close to each other. Next, the activated dictionary atoms are assembled together and passed to the next compound dictionary learning and coding layers. In this way, the activated atoms in the first layer can be represented by the deeper atoms in the second dictionary. Intuitively, the second dictionary is designed to learn the fine-grained components which are shared among the input dictionary atoms. In this way, a more informative and discriminative low-level representation of the dictionary atoms can be obtained. We empirically compare the proposed DDLCN with several dictionary learning methods and deep learning architectures. The experimental results on four popular benchmark datasets demonstrate that the proposed DDLCN achieves competitive results compared with state-of-the-art approaches.

Includes members:
  Member "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "matrix cofactorization for joint representation learning and supervised classification application to hyperspectral image analysis")
  Member "convolutional dictionary learning acceleration and convergence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional dictionary pair learning network for image representation learning")
  Member "structure aware classification using supervised dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding gems multi scale dictionaries for high dimensional graph signals")
  Member "disentangling factors of variation in deep representations using adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "greedy deep dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hp vpinns variational physics informed neural networks with domain decomposition")
  Member "discriminative bayesian dictionary learning for classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep dictionary learning a parametric network approach")
  Member "neural activation constellations unsupervised part model discovery with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interpretable cnns for object classification")
  Member "convolutional clustering for unsupervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "diversity regularized adversarial learning")

Question: Please tell me which class the source paper 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)