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: "deep extractor network for target speaker recovery from single channel speech mixtures"
  Abstract: Speaker-aware source separation methods are promising workarounds for major difficulties such as arbitrary source permutation and unknown number of sources. However, it remains challenging to achieve satisfying performance provided a very short available target speaker utterance (anchor). Here we present a novel "deep extractor network" which creates an extractor point for the target speaker in a canonical high dimensional embedding space, and pulls together the time-frequency bins corresponding to the target speaker. The proposed model is different from prior works in that the canonical embedding space encodes knowledges of both the anchor and the mixture during an end-to-end training phase: First, embeddings for the anchor and mixture speech are separately constructed in a primary embedding space, and then combined as an input to feed-forward layers to transform to a canonical embedding space which we discover more stable than the primary one. Experimental results show that given a very short utterance, the proposed model can efficiently recover high quality target speech from a mixture, which outperforms various baseline models, with 5.2% and 6.6% relative improvements in SDR and PESQ respectively compared with a baseline oracle deep attracor model. Meanwhile, we show it can be generalized well to more than one interfering speaker.

Connected through hyperedge HE-1 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "phase aware speech enhancement with deep complex u net"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "sdr half baked or well done"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "cracking the cocktail party problem by multi beam deep attractor network"
    - "end to end source separation with adaptive front ends"

Connected through hyperedge HE-2 (induced by source paper "audio query based music source separation"):
  with members:
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "end to end sound source separation conditioned on instrument labels"
    - "a style based generator architecture for generative adversarial networks"
    - "music source separation using stacked hourglass networks"
    - "class conditional embeddings for music source separation"
    - "sampling generative networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "multi scale multi band densenets for audio source separation"

Connected through hyperedge HE-3 (induced by source paper "the sound of my voice speaker representation loss for target voice separation"):
  with members:
    - "audio style transfer"
    - "voxceleb2 deep speaker recognition"
    - "deep attractor network for single microphone speaker separation"
    - "voxceleb a large scale speaker identification dataset"
    - "photographic image synthesis with cascaded refinement networks"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "improved techniques for training gans"
    - "wider or deeper revisiting the resnet model for visual recognition"

Connected through hyperedge HE-4 (induced by source paper "end to end anchored speech recognition"):
  with members:
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "end to end contextual speech recognition using class language models and a token passing decoder"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "state of the art speech recognition with sequence to sequence models"
    - "end to end text dependent speaker verification"
    - "attention based models for speech recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "discriminative learning for monaural speech separation using deep embedding features"):
  with members:
    - "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"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "class conditional embeddings for music source separation"):
  with members:
    - "speaker independent speech separation with deep attractor network"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "supervised speech separation based on deep learning an overview"
    - "single channel multi speaker separation using deep clustering"
    - "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)