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: "image super resolution by neural texture transfer"
  Abstract: Due to the significant information loss in low-resolution (LR) images, it has become extremely challenging to further advance the state-of-the-art of single image super-resolution (SISR). Reference-based super-resolution (RefSR), on the other hand, has proven to be promising in recovering high-resolution (HR) details when a reference (Ref) image with similar content as that of the LR input is given. However, the quality of RefSR can degrade severely when Ref is less similar. This paper aims to unleash the potential of RefSR by leveraging more texture details from Ref images with stronger robustness even when irrelevant Ref images are provided. Inspired by the recent work on image stylization, we formulate the RefSR problem as neural texture transfer. We design an end-to-end deep model which enriches HR details by adaptively transferring the texture from Ref images according to their textural similarity. Instead of matching content in the raw pixel space as done by previous methods, our key contribution is a multi-level matching conducted in the neural space. This matching scheme facilitates multi-scale neural transfer that allows the model to benefit more from those semantically related Ref patches, and gracefully degrade to SISR performance on the least relevant Ref inputs. We build a benchmark dataset for the general research of RefSR, which contains Ref images paired with LR inputs with varying levels of similarity. Both quantitative and qualitative evaluations demonstrate the superiority of our method over state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "extended feature pyramid network for small object detection"):
  with members:
    - "reppoints point set representation for object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"
    - "task driven super resolution object detection in low resolution images"

Connected through hyperedge HE-2 (induced by source paper "concurrently extrapolating and interpolating networks for continuous model generation"):
  with members:
    - "bi directional cascade network for perceptual edge detection"
    - "cfsnet toward a controllable feature space for image restoration"
    - "pixel adaptive convolutional neural networks"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "a general decoupled learning framework for parameterized image operators"
    - "a benchmark for edge preserving image smoothing"
    - "multiple description convolutional neural networks for image compression"

Connected through hyperedge HE-3 (induced by source paper "learning to have an ear for face super resolution"):
  with members:
    - "seeing what a gan cannot generate"
    - "what makes training multi modal networks hard"
    - "toward real world single image super resolution a new benchmark and a new model"
    - "two stream action recognition oriented video super resolution"
    - "ranksrgan generative adversarial networks with ranker for image super resolution"
    - "evaluating robustness of deep image super resolution against adversarial attacks"
    - "zoom to learn learn to zoom"
    - "speech2face learning the face behind a voice"

Connected through hyperedge HE-4 (induced by source paper "advpattern physical world attacks on deep person re identification via adversarially transformable patterns"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "age progression regression by conditional adversarial autoencoder"
    - "synthesizing robust adversarial examples"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "deep ranking for person re identification via joint representation learning"
    - "delving into transferable adversarial examples and black box attacks"

Connected through hyperedge HE-5 (induced by source paper "fisr deep joint frame interpolation and super resolution with a multi scale temporal loss"):
  with members:
    - "fast spatio temporal residual network for video super resolution"
    - "depth aware video frame interpolation"
    - "recurrent back projection network for video super resolution"
    - "learning for video super resolution through hr optical flow estimation"
    - "context aware synthesis for video frame interpolation"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-6 (induced by source paper "talking face generation by conditional recurrent adversarial network"):
  with members:
    - "video to video synthesis"
    - "decoupled learning for conditional adversarial networks"
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "dual motion gan for future flow embedded video prediction"
    - "decomposing motion and content for natural video sequence prediction"
    - "r btn cross domain face composite and synthesis from limited facial patches"
    - "obamanet photo realistic lip sync from text"

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)