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: "a fully convolutional neural network for speech enhancement"
  Abstract: In hearing aids, the presence of babble noise degrades hearing intelligibility of human speech greatly. However, removing the babble without creating artifacts in human speech is a challenging task in a low SNR environment. Here, we sought to solve the problem by finding a `mapping' between noisy speech spectra and clean speech spectra via supervised learning. Specifically, we propose using fully Convolutional Neural Networks, which consist of lesser number of parameters than fully connected networks. The proposed network, Redundant Convolutional Encoder Decoder (R-CED), demonstrates that a convolutional network can be 12 times smaller than a recurrent network and yet achieves better performance, which shows its applicability for an embedded system: the hearing aids.

Connected through hyperedge HE-1 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "on the relationship between short time objective intelligibility and short time spectral amplitude mean square error for speech enhancement"
    - "on the variance of the adaptive learning rate and beyond"
    - "a comprehensive study of speech separation spectrogram vs waveform separation"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "sdr half baked or well done"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"

Connected through hyperedge HE-2 (induced by source paper "improving gans for speech enhancement"):
  with members:
    - "towards generalized speech enhancement with generative adversarial networks"
    - "convolutional neural network based speech enhancement for cochlear implant recipients"
    - "segan speech enhancement generative adversarial network"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "speech enhancement in multiple noise conditions using deep neural networks"
    - "improved techniques for training gans"
    - "least squares generative adversarial networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "towards generalized speech enhancement with generative adversarial networks"):
  with members:
    - "high fidelity image generation with fewer labels"
    - "clarinet parallel wave generation in end to end text to speech"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "language and noise transfer in speech enhancement generative adversarial network"
    - "segan speech enhancement generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "deep speech enhancement for reverberated and noisy signals using wide residual networks"):
  with members:
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "convolutional recurrent neural networks for speech enhancement"
    - "segan speech enhancement generative adversarial network"
    - "decoupled weight decay regularization"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "wide residual networks"
    - "musan a music speech and noise corpus"

Connected through hyperedge HE-5 (induced by source paper "convolutional recurrent neural network based progressive learning for monaural speech enhancement"):
  with members:
    - "progressive image deraining networks a better and simpler baseline"
    - "constrained convolutional recurrent networks to improve speech quality with low impact on recognition accuracy"
    - "convolutional recurrent neural networks for speech enhancement"
    - "supervised speech separation based on deep learning an overview"
    - "pruning filters for efficient convnets"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deeply recursive convolutional network for image super resolution"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "coarse to fine optimization for speech enhancement"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"
    - "speech enhancement in multiple noise conditions using deep neural networks"
    - "generative image modeling using style and structure adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-7 (induced by source paper "voice command generation using progressive wavegans"):
  with members:
    - "generative adversarial networks gans what it can generate and what it cannot"
    - "improved training of generative adversarial networks using representative features"
    - "generative adversarial networks an overview"
    - "segan speech enhancement generative adversarial network"
    - "improved training of wasserstein gans"
    - "image to image translation with conditional adversarial networks"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-8 (induced by source paper "an exploration of mimic architectures for residual network based spectral mapping"):
  with members:
    - "spectral feature mapping with mimic loss for robust speech recognition"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "very deep convolutional networks for end to end speech recognition"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "very deep convolutional neural networks for robust speech recognition"
    - "the microsoft 2016 conversational speech recognition system"
    - "wide residual networks"
    - "deep residual learning for image recognition"

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.LG(Machine Learning)