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 attractor network for single microphone speaker separation"
  Abstract: Despite the overwhelming success of deep learning in various speech processing tasks, the problem of separating simultaneous speakers in a mixture remains challenging. Two major difficulties in such systems are the arbitrary source permutation and unknown number of sources in the mixture. We propose a novel deep learning framework for single channel speech separation by creating attractor points in high dimensional embedding space of the acoustic signals which pull together the time-frequency bins corresponding to each source. Attractor points in this study are created by finding the centroids of the sources in the embedding space, which are subsequently used to determine the similarity of each bin in the mixture to each source. The network is then trained to minimize the reconstruction error of each source by optimizing the embeddings. The proposed model is different from prior works in that it implements an end-to-end training, and it does not depend on the number of sources in the mixture. Two strategies are explored in the test time, K-means and fixed attractor points, where the latter requires no post-processing and can be implemented in real-time. We evaluated our system on Wall Street Journal dataset and show 5.49% improvement over the previous state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "la furca iterative context aware end to end monaural speech separation based on dual path deep parallel inter intra bi lstm with attention"):
  with members:
    - "is cqt more suitable for monaural speech separation than stft an empirical study"
    - "universal sound separation"
    - "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel speech separation"
    - "divide and conquer a deep casa approach to talker independent monaural speaker separation"
    - "spatial temporal recurrent neural network for emotion recognition"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"
    - "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-2 (induced by source paper "supervised speaker embedding de mixing in two speaker environment"):
  with members:
    - "learned in speech recognition contextual acoustic word embeddings"
    - "speaker independent speech separation with deep attractor network"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "voxceleb2 deep speaker recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "deep convolutional acoustic word embeddings using word pair side information"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "fact aware sentence split and rephrase with permutation invariant training"):
  with members:
    - "transforming complex sentences into a semantic hierarchy"
    - "split and rephrase better evaluation and a stronger baseline"
    - "learning to split and rephrase from wikipedia edit history"
    - "split and rephrase"
    - "attention is all you need"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-4 (induced by source paper "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "speaker independent speech separation with deep attractor network"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "cracking the cocktail party problem by multi beam deep attractor network"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "end to end source separation with adaptive front ends"
    - "single channel multi talker speech recognition with permutation invariant training"

Connected through hyperedge HE-5 (induced by source paper "deep audio prior"):
  with members:
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "audio denoising with deep network priors"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "self supervised generation of spatial audio for 360 video"
    - "single image reflection separation with perceptual losses"
    - "does k matter k nn hubness analysis for kernel additive modelling vocal separation"
    - "improving dnn based music source separation using phase features"
    - "wave u net a multi scale neural network for end to end audio source separation"

Connected through hyperedge HE-6 (induced by source paper "improved speech separation with time and frequency cross domain joint embedding and clustering"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "speaker independent speech separation with deep attractor network"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"
    - "segan speech enhancement generative adversarial network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "voxceleb a large scale speaker identification dataset"

Connected through hyperedge HE-7 (induced by source paper "region segmentation via deep learning and convex optimization"):
  with members:
    - "nesti net normal estimation for unstructured 3d point clouds using convolutional neural networks"
    - "pcpnet learning local shape properties from raw point clouds"
    - "deep projective 3d semantic segmentation"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "semantic instance segmentation with a discriminative loss function"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "octnet learning deep 3d representations at high resolutions"

Connected through hyperedge HE-8 (induced by source paper "furcanext end to end monaural speech separation with dynamic gated dilated temporal convolutional networks"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel speech separation"
    - "speaker independent speech separation with deep attractor network"
    - "accurate data efficient unconstrained text recognition with convolutional neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "single channel multi speaker separation using deep clustering"

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)