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: "wham extending speech separation to noisy environments"
  Abstract: Recent progress in separating the speech signals from multiple overlapping speakers using a single audio channel has brought us closer to solving the cocktail party problem. However, most studies in this area use a constrained problem setup, comparing performance when speakers overlap almost completely, at artificially low sampling rates, and with no external background noise. In this paper, we strive to move the field towards more realistic and challenging scenarios. To that end, we created the WSJ0 Hipster Ambient Mixtures (WHAM!) dataset, consisting of two speaker mixtures from the wsj0-2mix dataset combined with real ambient noise samples. The samples were collected in coffee shops, restaurants, and bars in the San Francisco Bay Area, and are made publicly available. We benchmark various speech separation architectures and objective functions to evaluate their robustness to noise. While separation performance decreases as a result of noise, we still observe substantial gains relative to the noisy signals for most approaches.

Connected through hyperedge HE-1 (induced by source paper "continuous speech separation dataset and analysis"):
  with members:
    - "furcanext end to end monaural speech separation with dynamic gated dilated temporal convolutional networks"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "simultaneous speech recognition and speaker diarization for monaural dialogue recordings with target speaker acoustic models"
    - "a comprehensive study of speech separation spectrogram vs waveform separation"
    - "pykaldi2 yet another speech toolkit based on kaldi and pytorch"
    - "sdr half baked or well done"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"

Connected through hyperedge HE-2 (induced by source paper "demystifying tasnet a dissecting approach"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "a comprehensive study of speech separation spectrogram vs waveform separation"
    - "sms wsj database performance measures and baseline recipe for multi channel source separation and recognition"
    - "end to end multi channel speech separation"
    - "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel speech separation"
    - "recursive speech separation for unknown number of speakers"
    - "end to end speech separation with unfolded iterative phase reconstruction"

Connected through hyperedge HE-3 (induced by source paper "improving voice separation by incorporating end to end speech recognition"):
  with members:
    - "recursive speech separation for unknown number of speakers"
    - "the conversation deep audio visual speech enhancement"
    - "espnet end to end speech processing toolkit"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "multi scale multi band densenets for audio source separation"
    - "aenet learning deep audio features for video analysis"
    - "automatic pronunciation generation by utilizing a semi supervised deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "whamr noisy and reverberant single channel speech separation"):
  with members:
    - "furcanext end to end monaural speech separation with dynamic gated dilated temporal convolutional networks"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "sdr half baked or well done"
    - "end to end monaural multi speaker asr system without pretraining"
    - "a purely end to end system for multi speaker speech recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "single channel multi speaker separation using deep clustering"

Connected through hyperedge HE-5 (induced by source paper "filterbank design for end to end speech separation"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "on the variance of the adaptive learning rate and beyond"
    - "lookahead optimizer k steps forward 1 step back"
    - "sdr half baked or well done"
    - "deep attractor network for single microphone speaker separation"
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "single channel multi speaker separation using deep clustering"

Connected through hyperedge HE-6 (induced by source paper "sms wsj database performance measures and baseline recipe for multi channel source separation and recognition"):
  with members:
    - "sdr half baked or well done"
    - "a purely end to end system for multi speaker speech recognition"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "deep clustering discriminative embeddings for segmentation and separation"

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)