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: "supervised deep sparse coding networks"
  Abstract: In this paper, we describe the deep sparse coding network (SCN), a novel deep network that encodes intermediate representations with nonnegative sparse coding. The SCN is built upon a number of cascading bottleneck modules, where each module consists of two sparse coding layers with relatively wide and slim dictionaries that are specialized to produce high dimensional discriminative features and low dimensional representations for clustering, respectively. During training, both the dictionaries and regularization parameters are optimized with an end-to-end supervised learning algorithm based on multilevel optimization. Effectiveness of an SCN with seven bottleneck modules is verified on several popular benchmark datasets. Remarkably, with few parameters to learn, our SCN achieves 5.81% and 19.93% classification error rate on CIFAR-10 and CIFAR-100, respectively.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "deep network classification by scattering and homotopy dictionary learning"):
  with members:
    - "deep dictionary learning a parametric network approach"
    - "scattering networks for hybrid representation learning"
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "kymatio scattering transforms in python"
    - "scaling the scattering transform deep hybrid networks"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "deep roto translation scattering for object classification"

Connected through hyperedge HE-3 (induced by source paper "dyan a dynamical atoms based network for video prediction"):
  with members:
    - "dual motion gan for future flow embedded video prediction"
    - "predicting deeper into the future of semantic segmentation"
    - "action tubelet detector for spatio temporal action localization"
    - "unsupervised learning of long term motion dynamics for videos"
    - "video frame synthesis using deep voxel flow"
    - "tube convolutional neural network t cnn for action detection in videos"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "deep learning for detecting multiple space time action tubes in videos"

Connected through hyperedge HE-4 (induced by source paper "real time semantic image segmentation via spatial sparsity"):
  with members:
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "bridging category level and instance level semantic image segmentation"
    - "identity mappings in deep residual networks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "residual networks behave like ensembles of relatively shallow 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)