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: "deep neural networks for no reference and full reference image quality assessment"
  Abstract: We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that: 1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and 2) it allows for joint learning of local quality and local weights, i.e., relative importance of local quality to the global quality estimate, in an unified framework. Our approach is purely data-driven and does not rely on hand-crafted features or other types of prior domain knowledge about the human visual system or image statistics. We evaluate the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the LIVE In the wild image quality challenge database and show superior performance to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation shows a high ability to generalize between different databases, indicating a high robustness of the learned features.

Connected through hyperedge HE-1 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment"):
  with members:
    - "i am going mad maximum discrepancy competition for comparing classifiers adaptively"
    - "learning to blindly assess image quality in the laboratory and wild"
    - "nima neural image assessment"
    - "on the use of deep learning for blind image quality assessment"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "end to end optimized image compression"

Connected through hyperedge HE-2 (induced by source paper "print defect mapping with semantic segmentation"):
  with members:
    - "handling inter annotator agreement for automated skin lesion segmentation"
    - "blind predicting similar quality map for image quality assessment"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "unmasking clever hans predictors and assessing what machines really learn"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "explaining the unique nature of individual gait patterns with deep learning"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "visualizing and understanding atari agents"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "socially aware motion planning with deep reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-4 (induced by source paper "ranksrgan generative adversarial networks with ranker for image super resolution"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "suppressing model overfitting for image super resolution networks"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the perception distortion tradeoff"
    - "ranking cgans subjective control over semantic image attributes"

Connected through hyperedge HE-5 (induced by source paper "robust and communication efficient federated learning from non iid data"):
  with members:
    - "towards federated learning at scale system design"
    - "compact and computationally efficient representation of deep neural networks"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "federated learning strategies for improving communication efficiency"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "variance based gradient compression for efficient distributed deep learning"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "how to backdoor federated learning"

Connected through hyperedge HE-6 (induced by source paper "icface interpretable and controllable face reenactment using gans"):
  with members:
    - "facelet bank for fast portrait manipulation"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "cr gan learning complete representations for multi view generation"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "ganimation anatomically aware facial animation from a single image"
    - "the contextual loss for image transformation with non aligned data"
    - "which training methods for gans do actually converge"
    - "learning image to image translation using paired and unpaired training samples"

Connected through hyperedge HE-7 (induced by source paper "an alarm system for segmentation algorithm based on shape model"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "uncertainty driven sanity check application to postoperative brain tumor cavity segmentation"
    - "training multi organ segmentation networks with sample selection by relaxed upper confident bound"
    - "real time prediction of segmentation quality"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "a 3d coarse to fine framework for volumetric medical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "learning to observe approximating human perceptual thresholds for detection of suprathreshold image transformations"):
  with members:
    - "contrast oriented deep neural networks for salient object detection"
    - "focal loss for dense object detection"
    - "sgdr stochastic gradient descent with restarts"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "image to image translation with conditional adversarial networks"
    - "visual saliency based on multiscale deep features"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)