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: "learning to hallucinate face images via component generation and enhancement"
  Abstract: We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methods

Connected through hyperedge HE-1 (induced by source paper "joint face completion and super resolution using multi scale feature relation learning"):
  with members:
    - "facial expression restoration based on improved graph convolutional networks"
    - "fcsr gan joint face completion and super resolution via multi task learning"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "geometry aware face completion and editing"
    - "graph neural networks a review of methods and applications"
    - "rain streak removal for single image via kernel guided cnn"
    - "learning a discriminative prior for blind image deblurring"
    - "cycle dehaze enhanced cyclegan for single image dehazing"

Connected through hyperedge HE-2 (induced by source paper "self enhanced convolutional network for facial video hallucination"):
  with members:
    - "frame recurrent video super resolution"
    - "the relativistic discriminator a key element missing from standard gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "residual dense network for image super resolution"
    - "efficient super resolution for large scale images using attentional gan"
    - "face super resolution through wasserstein gans"
    - "non local neural networks"
    - "deep laplacian pyramid networks for fast and accurate super resolution"

Connected through hyperedge HE-3 (induced by source paper "fcsr gan joint face completion and super resolution via multi task learning"):
  with members:
    - "feedback network for image super resolution"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "coherent semantic attention for image inpainting"
    - "pluralistic image completion"
    - "adaptive transform domain image super resolution via orthogonally regularized deep networks"
    - "geometry aware face completion and editing"
    - "tattoo image search at scale joint detection and compact representation learning"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-4 (induced by source paper "joint face super resolution and deblurring using a generative adversarial network"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "face de occlusion using 3d morphable model and generative adversarial network"
    - "arcface additive angular margin loss for deep face recognition"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-5 (induced by source paper "component attention guided face super resolution network cagface"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "feedback network for image super resolution"
    - "zoom to learn learn to zoom"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "the relativistic discriminator a key element missing from standard gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimedia super resolution via deep learning a survey"

Connected through hyperedge HE-6 (induced by source paper "identity aware deep face hallucination via adversarial face verification"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "sigan siamese generative adversarial network for identity preserving face hallucination"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "style and content disentanglement in generative adversarial networks"
    - "multibiometric secure system based on deep learning"
    - "attention aware face hallucination via deep reinforcement learning"
    - "biometrics as a service a framework to promote innovative biometric recognition in the cloud"

Connected through hyperedge HE-7 (induced by source paper "sigan siamese generative adversarial network for identity preserving face hallucination"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "surveillance face recognition challenge"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "attention aware face hallucination via deep reinforcement learning"
    - "super fan integrated facial landmark localization and super resolution of real world low resolution faces in arbitrary poses with gans"
    - "pixel recursive super resolution"
    - "deep joint face hallucination and recognition"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-8 (induced by source paper "face hallucination by attentive sequence optimization with reinforcement learning"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "facial landmark machines a backbone branches architecture with progressive representation learning"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "attention aware face hallucination via deep reinforcement learning"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "zero shot super resolution using deep internal learning"
    - "parallel multiscale autoregressive density estimation"

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)