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: "multi scale face restoration with sequential gating ensemble network"
  Abstract: Restoring face images from distortions is important in face recognition applications and is challenged by multiple scale issues, which is still not well-solved in research area. In this paper, we present a Sequential Gating Ensemble Network (SGEN) for multi-scale face restoration issue. We first employ the principle of ensemble learning into SGEN architecture design to reinforce predictive performance of the network. The SGEN aggregates multi-level base-encoders and base-decoders into the network, which enables the network to contain multiple scales of receptive field. Instead of combining these base-en/decoders directly with non-sequential operations, the SGEN takes base-en/decoders from different levels as sequential data. Specifically, the SGEN learns to sequentially extract high level information from base-encoders in bottom-up manner and restore low level information from base-decoders in top-down manner. Besides, we propose to realize bottom-up and top-down information combination and selection with Sequential Gating Unit (SGU). The SGU sequentially takes two inputs from different levels and decides the output based on one active input. Experiment results demonstrate that our SGEN is more effective at multi-scale human face restoration with more image details and less noise than state-of-the-art image restoration models. By using adversarial training, SGEN also produces more visually preferred results than other models through subjective evaluation.

Connected through hyperedge HE-1 (induced by source paper "image to image translation with multi path consistency regularization"):
  with members:
    - "conditional image to image translation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "dualgan unsupervised dual learning for image to image translation"
    - "unsupervised cross domain image generation"
    - "stacked generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-2 (induced by source paper "distribution discrepancy maximization for image privacy preserving"):
  with members:
    - "conditional image to image translation"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "perceptual losses for real time style transfer and super resolution"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "faceless person recognition privacy implications in social media"
    - "defeating image obfuscation with deep learning"
    - "universal adversarial perturbations"

Connected through hyperedge HE-3 (induced by source paper "deepir a deep semantics driven framework for image retargeting"):
  with members:
    - "conditional image to image translation"
    - "weakly and self supervised learning for content aware deep image retargeting"
    - "visual attribute transfer through deep image analogy"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)