human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning to have an ear for face super resolution"):
  Abstract: We propose a novel method to use both audio and a low-resolution image to perform extreme face super-resolution (a 16x increase of the input size). When the resolution of the input image is very low (e.g., 8x8 pixels), the loss of information is so dire that important details of the original identity have been lost and audio can aid the recovery of a plausible high-resolution image. In fact, audio carries information about facial attributes, such as gender and age. Moreover, if an audio track belongs to an identity in a known training set, such audio might even help to restore the original identity. Towards this goal, we propose a model and a training procedure to extract information about the face of a person from her audio track and to combine it with the information extracted from her low-resolution image, which relates more to pose and colors of the face. We demonstrate that the combination of these two inputs yields high-resolution images that better capture the correct attributes of the face. In particular, we show experimentally that audio can assist in recovering attributes such as the gender, the age and the identity, and thus improve the correctness of the image reconstruction process. Our procedure does not make use of human annotation and thus can be easily trained with existing video datasets. Moreover, we show that our model builds a factorized representation of images and audio as it allows one to mix low-resolution images and audio from different videos and to generate realistic faces with semantically meaningful combinations.

Includes members:
  Member "seeing what a gan cannot generate":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")
  Member "what makes training multi modal networks hard":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "imvotenet boosting 3d object detection in point clouds with image votes")
  Member "toward real world single image super resolution a new benchmark and a new model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "two stream action recognition oriented video super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "is there tradeoff between spatial and temporal in video super resolution")
  Member "image super resolution by neural texture transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "extended feature pyramid network for small object detection")
  Member "ranksrgan generative adversarial networks with ranker for image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "closed loop matters dual regression networks for single image super resolution")
  Member "evaluating robustness of deep image super resolution against adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving robustness of deep learning based image reconstruction")
  Member "zoom to learn learn to zoom":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "perceptual image super resolution with progressive adversarial network")

Question: Please tell me which class the source paper 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)