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 "using recurrences in time and frequency within u net architecture for speech enhancement"):
  Abstract: When designing fully-convolutional neural network, there is a trade-off between receptive field size, number of parameters and spatial resolution of features in deeper layers of the network. In this work we present a novel network design based on combination of many convolutional and recurrent layers that solves these dilemmas. We compare our solution with U-nets based models known from the literature and other baseline models on speech enhancement task. We test our solution on TIMIT speech utterances combined with noise segments extracted from NOISEX-92 database and show clear advantage of proposed solution in terms of SDR (signal-to-distortion ratio), SIR (signal-to-interference ratio) and STOI (spectro-temporal objective intelligibility) metrics compared to the current state-of-the-art.

Includes members:
  Member "convolutional recurrent neural networks for speech enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep speech enhancement for reverberated and noisy signals using wide residual networks")
  Member "deep learning for environmentally robust speech recognition an overview of recent developments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "single channel speech separation with auxiliary speaker embeddings")
  Member "supervised speech separation based on deep learning an overview":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")
  Member "single channel audio source separation using convolutional denoising autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")
  Member "a fully convolutional neural network for speech enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "joint optimization of masks and deep recurrent neural networks for monaural source separation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "source separation with deep generative priors")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "renet a recurrent neural network based alternative to convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference")

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.LG(Machine Learning)