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: "inpainting of long audio segments with similarity graphs"
  Abstract: We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that encodes signal structure in terms of time-persistent spectral similarity. A suitable candidate segment for the substitution of the lost content is proposed by an intuitive optimization scheme and smoothly inserted into the gap, i.e. the lost or distorted signal region. Extensive listening tests show that the proposed algorithm provides highly promising results when applied to a variety of real-world music signals.

Connected through hyperedge HE-1 (induced by source paper "vision infused deep audio inpainting"):
  with members:
    - "compound domain adaptation in an open world"
    - "recursive visual sound separation using minus plus net"
    - "the sound of motions"
    - "learning to localize sound source in visual scenes"
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "self supervised generation of spatial audio for 360 video"

Connected through hyperedge HE-2 (induced by source paper "deep long audio inpainting"):
  with members:
    - "deep video inpainting"
    - "adversarial generation of time frequency features with application in audio synthesis"
    - "free form video inpainting with 3d gated convolution and temporal patchgan"
    - "learnable gated temporal shift module for deep video inpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "image inpainting for irregular holes using partial convolutions"
    - "a context encoder for audio inpainting"
    - "efficient neural audio synthesis"

Connected through hyperedge HE-3 (induced by source paper "learning to traverse latent spaces for musical score inpainting"):
  with members:
    - "music generation by deep learning challenges and directions"
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "this time with feeling learning expressive musical performance"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "the machine learning algorithm as creative musical tool"
    - "a predictive model for music based on learned interval representations"
    - "music transformer"
    - "audio to score alignment using transposition invariant features"

Connected through hyperedge HE-4 (induced by source paper "deep speech inpainting of time frequency masks"):
  with members:
    - "incorporating symbolic sequential modeling for speech enhancement"
    - "spectrogram feature losses for music source separation"
    - "divide and conquer a deep casa approach to talker independent monaural speaker separation"
    - "towards generalized speech enhancement with generative adversarial networks"
    - "a context encoder for audio inpainting"
    - "generative image inpainting with contextual attention"
    - "supervised speech separation based on deep learning an overview"
    - "a hybrid dsp deep learning approach to real time full band speech enhancement"

Connected through hyperedge HE-5 (induced by source paper "a context encoder for audio inpainting"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "introducing spain sparse audion inpainter"
    - "phase reconstruction from amplitude spectrograms based on von mises distribution deep neural network"
    - "adversarial audio synthesis"
    - "a neural parametric singing synthesizer"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "searching for activation functions"

Connected through hyperedge HE-6 (induced by source paper "introducing spain sparse audion inpainter"):
  with members:
    - "a context encoder for audio inpainting"
    - "a modeling and algorithmic framework for non social co sparse audio restoration"
    - "sparsity and cosparsity for audio declipping a flexible non convex approach"

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.SD(Sound)