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: "audio super resolution using neural networks"
  Abstract: We introduce a new audio processing technique that increases the sampling rate of signals such as speech or music using deep convolutional neural networks. Our model is trained on pairs of low and high-quality audio examples; at test-time, it predicts missing samples within a low-resolution signal in an interpolation process similar to image super-resolution. Our method is simple and does not involve specialized audio processing techniques; in our experiments, it outperforms baselines on standard speech and music benchmarks at upscaling ratios of 2x, 4x, and 6x. The method has practical applications in telephony, compression, and text-to-speech generation; it demonstrates the effectiveness of feed-forward convolutional architectures on an audio generation task.

Connected through hyperedge HE-1 (induced by source paper "a unified neural architecture for instrumental audio tasks"):
  with members:
    - "voice impersonation using generative adversarial networks"
    - "segan speech enhancement generative adversarial network"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "image to image translation with conditional adversarial networks"
    - "wavenet a generative model for raw audio"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "self supervised generation of spatial audio for 360 video"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "the sound of pixels"
    - "audio visual scene analysis with self supervised multisensory features"
    - "supervised speech separation based on deep learning an overview"
    - "identity mappings in deep residual networks"
    - "wavenet a generative model for raw audio"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised deep recurrent neural networks for basic dance step generation"):
  with members:
    - "weakly supervised hierarchical text classification"
    - "end to end musical key estimation using a convolutional neural network"
    - "madmom a new python audio and music signal processing library"
    - "audio event detection using weakly labeled data"
    - "generative choreography using deep learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

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)