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: "global local face upsampling network"
  Abstract: Face hallucination, which is the task of generating a high-resolution face image from a low-resolution input image, is a well-studied problem that is useful in widespread application areas. Face hallucination is particularly challenging when the input face resolution is very low (e.g., 10 x 12 pixels) and/or the image is captured in an uncontrolled setting with large pose and illumination variations. In this paper, we revisit the algorithm introduced in [1] and present a deep interpretation of this framework that achieves state-of-the-art under such challenging scenarios. In our deep network architecture the global and local constraints that define a face can be efficiently modeled and learned end-to-end using training data. Conceptually our network design can be partitioned into two sub-networks: the first one implements the holistic face reconstruction according to global constraints, and the second one enhances face-specific details and enforces local patch statistics. We optimize the deep network using a new loss function for super-resolution that combines reconstruction error with a learned face quality measure in adversarial setting, producing improved visual results. We conduct extensive experiments in both controlled and uncontrolled setups and show that our algorithm improves the state of the art both numerically and visually.

Connected through hyperedge HE-1 (induced by source paper "facial attribute capsules for noise face super resolution"):
  with members:
    - "occlusion robust face recognition based on mask learning with pairwisedifferential siamese network"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "decorrelated adversarial learning for age invariant face recognition"
    - "mvf net multi view 3d face morphable model regression"
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "residual dense network for image super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"

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 "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-4 (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"
    - "deep joint face hallucination and recognition"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "learning warped guidance for blind face restoration"):
  with members:
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "deep face deblurring"
    - "generative face completion"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "motion deblurring in the wild"
    - "attention aware face hallucination via deep reinforcement learning"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "fsrnet end to end learning face super resolution with facial priors"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "generative face completion"
    - "pose invariant face alignment with a single cnn"
    - "attention aware face hallucination via deep reinforcement learning"
    - "scale aware face detection"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "memnet a persistent memory network for image restoration"
    - "learning to hallucinate face images via component generation and enhancement"

Connected through hyperedge HE-8 (induced by source paper "attention aware face hallucination via deep reinforcement learning"):
  with members:
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "tree structured reinforcement learning for sequential object localization"
    - "deep cascaded bi network for face hallucination"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "dynamic memory networks for visual and textual question answering"
    - "deepid3 face recognition with very deep neural networks"
    - "spatial transformer networks"
    - "naive deep face recognition touching the limit of lfw benchmark or not"

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)