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: "hallucinated iqa no reference image quality assessment via adversarial learning"
  Abstract: No-reference image quality assessment (NR-IQA) is a fundamental yet challenging task in low-level computer vision community. The difficulty is particularly pronounced for the limited information, for which the corresponding reference for comparison is typically absent. Although various feature extraction mechanisms have been leveraged from natural scene statistics to deep neural networks in previous methods, the performance bottleneck still exists. In this work, we propose a hallucination-guided quality regression network to address the issue. We firstly generate a hallucinated reference constrained on the distorted image, to compensate the absence of the true reference. Then, we pair the information of hallucinated reference with the distorted image, and forward them to the regressor to learn the perceptual discrepancy with the guidance of an implicit ranking relationship within the generator, and therefore produce the precise quality prediction. To demonstrate the effectiveness of our approach, comprehensive experiments are evaluated on four popular image quality assessment benchmarks. Our method significantly outperforms all the previous state-of-the-art methods by large margins. The code and model will be publicly available on the project page this https URL

Connected through hyperedge HE-1 (induced by source paper "deeperforensics 1 0 a large scale dataset for real world face forgery detection"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "the deepfake detection challenge dfdc preview dataset"
    - "celeb df a new dataset for deepfake forensics"
    - "look at boundary a boundary aware face alignment algorithm"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"

Connected through hyperedge HE-2 (induced by source paper "transgaga geometry aware unsupervised image to image translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "synthesizing images of humans in unseen poses"
    - "every smile is unique landmark guided diverse smile generation"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "unsupervised discovery of object landmarks as structural representations"
    - "a variational u net for conditional appearance and shape generation"

Connected through hyperedge HE-3 (induced by source paper "make a face towards arbitrary high fidelity face manipulation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "on learning 3d face morphable model from in the wild images"
    - "pose guided human video generation"
    - "look at boundary a boundary aware face alignment algorithm"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "self supervised learning of a facial attribute embedding from video"
    - "blind predicting similar quality map for image quality assessment"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "cross modal image fusion theory guided by subjective visual attention"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "densefuse a fusion approach to infrared and visible images"
    - "a cross modal image fusion theory guided by human visual characteristics"
    - "infrared and visible image fusion using a deep learning framework"
    - "non local neural networks"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"
    - "squeeze and excitation networks"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "reenactgan learning to reenact faces via boundary transfer"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "densereg fully convolutional dense shape regression in the wild"
    - "style aggregated network for facial landmark detection"
    - "densepose dense human pose estimation in the wild"
    - "quantifying facial age by posterior of age comparisons"
    - "deep structured learning for facial action unit intensity estimation"
    - "be your own prada fashion synthesis with structural coherence"

Connected through hyperedge HE-6 (induced by source paper "learning to predict image based rendering artifacts with respect to a hidden reference image"):
  with members:
    - "gans nqm a generative adversarial networks based no reference quality assessment metric for rgb d synthesized views"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "on the use of deep learning for blind image quality assessment"
    - "image to image translation with conditional adversarial networks"
    - "virtual rephotography novel view prediction error for 3d reconstruction"
    - "u net convolutional networks for biomedical image segmentation"

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)