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 "deep long audio inpainting"):
  Abstract: Long (> 200 ms) audio inpainting, to recover a long missing part in an audio segment, could be widely applied to audio editing tasks and transmission loss recovery. It is a very challenging problem due to the high dimensional, complex and non-correlated audio features. While deep learning models have made tremendous progress in image and video inpainting, audio inpainting did not attract much attention. In this work, we take a pioneering step, exploring the possibility of adapting deep learning frameworks from various domains inclusive of audio synthesis and image inpainting for audio inpainting. Also, as the first to systematically analyze factors affecting audio inpainting performance, we explore how factors ranging from mask size, receptive field and audio representation could affect the performance. We also set up a benchmark for long audio inpainting. The code will be available on GitHub upon accepted.

Includes members:
  Member "deep video inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "motion excited sampler video adversarial attack with sparked prior")
  Member "adversarial generation of time frequency features with application in audio synthesis":
    also connected through other hyperedges:
      (none)
  Member "free form video inpainting with 3d gated convolution and temporal patchgan":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gated texture cnn for efficient and configurable image denoising")
  Member "learnable gated temporal shift module for deep video inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "free form video inpainting with 3d gated convolution and temporal patchgan")
  Member "edgeconnect generative image inpainting with adversarial edge learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "guidance and evaluation semantic aware image inpainting for mixed scenes")
  Member "image inpainting for irregular holes using partial convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "symmetric skip connection wasserstein gan for high resolution facial image inpainting")
  Member "a context encoder for audio inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial generation of time frequency features with application in audio synthesis")
  Member "efficient neural audio synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "what is the state of neural network pruning")

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.SD(Sound)