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: "expandnet a deep convolutional neural network for high dynamic range expansion from low dynamic range content"
  Abstract: High dynamic range (HDR) imaging provides the capability of handling real world lighting as opposed to the traditional low dynamic range (LDR) which struggles to accurately represent images with higher dynamic range. However, most imaging content is still available only in LDR. This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet. ExpandNet accepts LDR images as input and generates images with an expanded range in an end-to-end fashion. The model attempts to reconstruct missing information that was lost from the original signal due to quantization, clipping, tone mapping or gamma correction. The added information is reconstructed from learned features, as the network is trained in a supervised fashion using a dataset of HDR images. The approach is fully automatic and data driven; it does not require any heuristics or human expertise. ExpandNet uses a multiscale architecture which avoids the use of upsampling layers to improve image quality. The method performs well compared to expansion/inverse tone mapping operators quantitatively on multiple metrics, even for badly exposed inputs.

Connected through hyperedge HE-1 (induced by source paper "the mertens unrolled network mu net a high dynamic range fusion neural network for through the windshield driver recognition"):
  with members:
    - "high dynamic range image forensics using cnn"
    - "retinaface single stage dense face localisation in the wild"
    - "arcface additive angular margin loss for deep face recognition"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "recurrent inference machines for solving inverse problems"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "separating reflection and transmission images in the wild"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-2 (induced by source paper "fhdr hdr image reconstruction from a single ldr image using feedback network"):
  with members:
    - "feedback network for image super resolution"
    - "deep chain hdri reconstructing a high dynamic range image from a single low dynamic range image"
    - "residual dense network for image super resolution"
    - "image correction via deep reciprocating hdr transformation"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "learning high dynamic range from outdoor panoramas"
    - "perceptual losses for real time style transfer and super resolution"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "hybrid loss for learning single image based hdr reconstruction"):
  with members:
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "deep chain hdri reconstructing a high dynamic range image from a single low dynamic range image"
    - "image correction via deep reciprocating hdr transformation"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"

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)