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: "volumetric super resolution of multispectral data"
  Abstract: Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7 ETM+) provide low-spatial high-spectral resolution multispectral (MS) or high-spatial low-spectral resolution panchromatic (PAN) images, separately. In order to reconstruct a high-spatial/high-spectral resolution multispectral image volume, either the information in MS and PAN images are fused (i.e. pansharpening) or super-resolution reconstruction (SRR) is used with only MS images captured on different dates. Existing methods do not utilize temporal information of MS and high spatial resolution of PAN images together to improve the resolution. In this paper, we propose a multiframe SRR algorithm using pansharpened MS images, taking advantage of both temporal and spatial information available in multispectral imagery, in order to exceed spatial resolution of given PAN images. We first apply pansharpening to a set of multispectral images and their corresponding PAN images captured on different dates. Then, we use the pansharpened multispectral images as input to the proposed wavelet-based multiframe SRR method to yield full volumetric SRR. The proposed SRR method is obtained by deriving the subband relations between multitemporal MS volumes. We demonstrate the results on Landsat 7 ETM+ images comparing our method to conventional techniques.

Connected through hyperedge HE-1 (induced by source paper "semantic estimation of 3d body shape and pose using minimal cameras"):
  with members:
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "volumetric performance capture from minimal camera viewpoints"
    - "exploiting temporal information for 3d human pose estimation"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "recurrent 3d pose sequence machines"
    - "convolutional pose machines"

Connected through hyperedge HE-2 (induced by source paper "deep autoencoder for combined human pose estimation and body model upscaling"):
  with members:
    - "multimedia super resolution via deep learning a survey"
    - "lstm pose machines"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "recurrent 3d pose sequence machines"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "learning to fuse 2d and 3d image cues for monocular body pose estimation"
    - "structured prediction of 3d human pose with deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "volumetric performance capture from minimal camera viewpoints"):
  with members:
    - "4d temporally coherent light field video"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "vconv dae deep volumetric shape learning without object labels"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "deep residual learning for image recognition"
    - "training very deep networks"
    - "deep networks for image super resolution with sparse prior"
    - "3d shapenets a deep representation for volumetric shapes"

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)