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 joint face hallucination and recognition"
  Abstract: Deep models have achieved impressive performance for face hallucination tasks. However, we observe that directly feeding the hallucinated facial images into recog- nition models can even degrade the recognition performance despite the much better visualization quality. In this paper, we address this problem by jointly learning a deep model for two tasks, i.e. face hallucination and recognition. In particular, we design an end-to-end deep convolution network with hallucination sub-network cascaded by recognition sub-network. The recognition sub- network are responsible for producing discriminative feature representations using the hallucinated images as inputs generated by hallucination sub-network. During training, we feed LR facial images into the network and optimize the parameters by minimizing two loss items, i.e. 1) face hallucination loss measured by the pixel wise difference between the ground truth HR images and network-generated images; and 2) verification loss which is measured by the classification error and intra-class distance. We extensively evaluate our method on LFW and YTF datasets. The experimental results show that our method can achieve recognition accuracy 97.95% on 4x down-sampled LFW testing set, outperforming the accuracy 96.35% of conventional face recognition model. And on the more challenging YTF dataset, we achieve recognition accuracy 90.65%, a margin over the recognition accuracy 89.45% obtained by conventional face recognition model on the 4x down-sampled version.

Connected through hyperedge HE-1 (induced by source paper "bridging the gap between computational photography and visual recognition"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"
    - "single image deraining a comprehensive benchmark analysis"
    - "dual directed capsule network for very low resolution image recognition"
    - "vision meets drones a challenge"
    - "the perception distortion tradeoff"
    - "crafting a toolchain for image restoration by deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "fan feature adaptation network for surveillance face recognition and normalization"):
  with members:
    - "a two step disentanglement method"
    - "srobb targeted perceptual loss for single image super resolution"
    - "toward real world single image super resolution a new benchmark and a new model"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "cosface large margin cosine loss for deep face recognition"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "super identity convolutional neural network for face hallucination"

Connected through hyperedge HE-3 (induced by source paper "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"):
  with members:
    - "bridging the gap between computational photography and visual recognition"
    - "vision meets drones a challenge"
    - "the unreasonable effectiveness of texture transfer for single image super resolution"
    - "yolov3 an incremental improvement"
    - "task driven super resolution object detection in low resolution images"
    - "learning to see in the dark"
    - "enhancing the performance of convolutional neural networks on quality degraded datasets"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-4 (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-5 (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"
    - "learning to hallucinate face images via component generation and enhancement"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-6 (induced by source paper "cross resolution face recognition via prior aided face hallucination and residual knowledge distillation"):
  with members:
    - "low resolution face recognition in the wild via selective knowledge distillation"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "look across elapse disentangled representation learning and photorealistic cross age face synthesis for age invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "super identity convolutional neural network for face hallucination"
    - "adversarial network compression"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "adversarial discriminative heterogeneous face recognition"

Connected through hyperedge HE-7 (induced by source paper "face hallucination revisited an exploratory study on dataset bias"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "disentangling adversarial robustness and generalization"
    - "face hallucination using cascaded super resolution and identity priors"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "domain adaptation for visual applications a comprehensive survey"
    - "generative face completion"
    - "pose invariant face alignment with a single cnn"

Connected through hyperedge HE-8 (induced by source paper "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"):
  with members:
    - "combining data driven and model driven methods for robust facial landmark detection"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "perceptual losses for real time style transfer and super resolution"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "learning from simulated and unsupervised images through adversarial training"
    - "learning residual images for face attribute manipulation"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"

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)