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 "listen to the image"):
  Abstract: Visual-to-auditory sensory substitution devices can assist the blind in sensing the visual environment by translating the visual information into a sound pattern. To improve the translation quality, the task performances of the blind are usually employed to evaluate different encoding schemes. In contrast to the toilsome human-based assessment, we argue that machine model can be also developed for evaluation, and more efficient. To this end, we firstly propose two distinct cross-modal perception model w.r.t. the late-blind and congenitally-blind cases, which aim to generate concrete visual contents based on the translated sound. To validate the functionality of proposed models, two novel optimization strategies w.r.t. the primary encoding scheme are presented. Further, we conduct sets of human-based experiments to evaluate and compare them with the conducted machine-based assessments in the cross-modal generation task. Their highly consistent results w.r.t. different encoding schemes indicate that using machine model to accelerate optimization evaluation and reduce experimental cost is feasible to some extent, which could dramatically promote the upgrading of encoding scheme then help the blind to improve their visual perception ability.

Includes members:
  Member "deep binary reconstruction for cross modal hashing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on deep hashing methods")
  Member "emnist an extension of mnist to handwritten letters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "visual to sound generating natural sound for videos in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "autofoley artificial synthesis of synchronized sound tracks for silent videos with deep learning")
  Member "deep cross modal audio visual generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis")
  Member "you said that":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")
  Member "cnn architectures for large scale audio classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi modal dense video captioning")
  Member "improved techniques for training gans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "generative adversarial text to image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)