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: "texture and structure incorporated scatternet hybrid deep learning network ts shdl for brain matter segmentation"
  Abstract: Automation of brain matter segmentation from MR images is a challenging task due to the irregular boundaries between the grey and white matter regions. In addition, the presence of intensity inhomogeneity in the MR images further complicates the problem. In this paper, we propose a texture and vesselness incorporated version of the ScatterNet Hybrid Deep Learning Network (TS-SHDL) that extracts hierarchical invariant mid-level features, used by fisher vector encoding and a conditional random field (CRF) to perform the desired segmentation. The performance of the proposed network is evaluated by extensive experimentation and comparison with the state-of-the-art methods on several 2D MRI scans taken from the synthetic McGill Brain Web as well as on the MRBrainS dataset of real 3D MRI scans. The advantages of the TS-SHDL network over supervised deep learning networks is also presented in addition to its superior performance over the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "generative scatternet hybrid deep learning g shdl network with structural priors for semantic image segmentation"):
  with members:
    - "discriminative training of deep fully connected continuous crfs with task specific loss"
    - "dual tree wavelet scattering network with parametric log transformation for object classification"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "multi path feedback recurrent neural network for scene parsing"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "pcanet a simple deep learning baseline for image classification"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "eye in the sky real time drone surveillance system dss for violent individuals identification using scatternet hybrid deep learning network"):
  with members:
    - "generative scatternet hybrid deep learning g shdl network with structural priors for semantic image segmentation"
    - "efficient convolutional network learning using parametric log based dual tree wavelet scatternet"
    - "dual tree wavelet scattering network with parametric log transformation for object classification"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "feature pyramid networks for object detection"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "flowing convnets for human pose estimation in videos"
    - "robust optimization for deep regression"

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)