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: "speaker independent speech separation with deep attractor network"
  Abstract: Despite the recent success of deep learning for many speech processing tasks, single-microphone, speaker-independent speech separation remains challenging for two main reasons. The first reason is the arbitrary order of the target and masker speakers in the mixture (permutation problem), and the second is the unknown number of speakers in the mixture (output dimension problem). We propose a novel deep learning framework for speech separation that addresses both of these issues. We use a neural network to project the time-frequency representation of the mixture signal into a high-dimensional embedding space. A reference point (attractor) is created in the embedding space to represent each speaker which is defined as the centroid of the speaker in the embedding space. The time-frequency embeddings of each speaker are then forced to cluster around the corresponding attractor point which is used to determine the time-frequency assignment of the speaker. We propose three methods for finding the attractors for each source in the embedding space and compare their advantages and limitations. The objective function for the network is standard signal reconstruction error which enables end-to-end operation during both training and test phases. We evaluated our system using the Wall Street Journal dataset (WSJ0) on two and three speaker mixtures and report comparable or better performance than other state-of-the-art deep learning methods for speech separation.

Connected through hyperedge HE-1 (induced by source paper "supervised speaker embedding de mixing in two speaker environment"):
  with members:
    - "learned in speech recognition contextual acoustic word embeddings"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "voxceleb2 deep speaker recognition"
    - "deep attractor network for single microphone speaker separation"
    - "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-2 (induced by source paper "ablation studies in artificial neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "observe and look further achieving consistent performance on atari"
    - "rainbow combining improvements in deep reinforcement learning"
    - "network dissection quantifying interpretability of deep visual representations"
    - "hindsight experience replay"

Connected through hyperedge HE-3 (induced by source paper "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "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"
    - "deep attractor network for single microphone speaker separation"
    - "single channel multi talker speech recognition with permutation invariant training"

Connected through hyperedge HE-4 (induced by source paper "divide and conquer a deep casa approach to talker independent monaural speaker 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"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "multi scale multi band densenets for audio source separation"
    - "regularizing and optimizing lstm language models"
    - "recurrent dropout without memory loss"

Connected through hyperedge HE-5 (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"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"
    - "deep attractor network for single microphone speaker separation"
    - "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-6 (induced by source paper "improving universal sound separation using sound classification"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "universal sound separation"
    - "chive varying prosody in speech synthesis with a linguistically driven dynamic hierarchical conditional variational network"
    - "sdr half baked or well done"
    - "class conditional embeddings for music source separation"
    - "differentiable consistency constraints for improved deep speech enhancement"
    - "unsupervised learning of semantic audio representations"
    - "conditional image text embedding networks"

Connected through hyperedge HE-7 (induced by source paper "universal sound separation"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "fixup initialization residual learning without normalization"
    - "sdr half baked or well done"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "differentiable consistency constraints for improved deep speech enhancement"
    - "exploring tradeoffs in models for low latency speech enhancement"
    - "a joint separation classification model for sound event detection of weakly labelled data"

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"
    - "accurate data efficient unconstrained text recognition with convolutional neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "deep attractor network for single microphone speaker separation"
    - "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)