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: "recover canonical view faces in the wild with deep neural networks"
  Abstract: Face images in the wild undergo large intra-personal variations, such as poses, illuminations, occlusions, and low resolutions, which cause great challenges to face-related applications. This paper addresses this challenge by proposing a new deep learning framework that can recover the canonical view of face images. It dramatically reduces the intra-person variances, while maintaining the inter-person discriminativeness. Unlike the existing face reconstruction methods that were either evaluated in controlled 2D environment or employed 3D information, our approach directly learns the transformation from the face images with a complex set of variations to their canonical views. At the training stage, to avoid the costly process of labeling canonical-view images from the training set by hand, we have devised a new measurement to automatically select or synthesize a canonical-view image for each identity. As an application, this face recovery approach is used for face verification. Facial features are learned from the recovered canonical-view face images by using a facial component-based convolutional neural network. Our approach achieves the state-of-the-art performance on the LFW dataset.

Connected through hyperedge HE-1 (induced by source paper "face hallucination with finishing touches"):
  with members:
    - "representation learning by rotating your faces"
    - "multi prototype networks for unconstrained set based face recognition"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "learning to see in the dark"
    - "non local neural networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-2 (induced by source paper "fully featured attribute transfer"):
  with members:
    - "modular generative adversarial networks"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "semi supervised fusedgan for conditional image generation"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "dna gan learning disentangled representations from multi attribute images"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "statistical transformer networks learning shape and appearance models via self supervision"):
  with members:
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "convolutional neural network architecture for geometric matching"
    - "deformable convolutional networks"
    - "3d morphable models as spatial transformer networks"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "toward end to end face recognition through alignment learning"
    - "dense transformer networks"
    - "faceposenet making a case for landmark free face alignment"

Connected through hyperedge HE-4 (induced by source paper "deep learning architectures for face recognition in video surveillance"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "an analysis of deep neural network models for practical applications"
    - "towards the design of an end to end automated system for image and video based recognition"
    - "towards automatic image editing learning to see another you"
    - "facenet a unified embedding for face recognition and clustering"
    - "going deeper with convolutions"
    - "deep learning face representation by joint identification verification"
    - "deeply learned face representations are sparse selective and robust"

Connected through hyperedge HE-5 (induced by source paper "learning discriminative features with class encoder"):
  with members:
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"
    - "deeply learned face representations are sparse selective and robust"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "what regularized auto encoders learn from the data generating distribution"

Connected through hyperedge HE-6 (induced by source paper "learning deep representation for face alignment with auxiliary attributes"):
  with members:
    - "surpassing human level face verification performance on lfw with gaussianface"
    - "deep learning face representation by joint identification verification"
    - "deep learning multi view representation for face recognition"
    - "deep regression for face alignment"
    - "heterogeneous multi task learning for human pose estimation with deep convolutional neural network"
    - "a convex formulation for learning task relationships in multi task learning"

Connected through hyperedge HE-7 (induced by source paper "a comprehensive survey on pose invariant face recognition"):
  with members:
    - "multi view intact space learning"
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "robust face recognition via multimodal deep face representation"
    - "facenet a unified embedding for face recognition and clustering"
    - "facial feature point detection a comprehensive survey"
    - "effective face frontalization in unconstrained images"
    - "deeply coupled auto encoder networks for cross view classification"
    - "web scale training for face identification"

Connected through hyperedge HE-8 (induced by source paper "deep learning and face recognition the state of the art"):
  with members:
    - "deepid3 face recognition with very deep neural networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "naive deep face recognition touching the limit of lfw benchmark or not"
    - "effective face frontalization in unconstrained images"
    - "learning face representation from scratch"
    - "imagenet large scale visual recognition challenge"
    - "deeply learned face representations are sparse selective and robust"
    - "practical recommendations for gradient based training of deep architectures"

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)