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 "cracking the cocktail party problem by multi beam deep attractor network"):
  Abstract: While recent progresses in neural network approaches to single-channel speech separation, or more generally the cocktail party problem, achieved significant improvement, their performance for complex mixtures is still not satisfactory. In this work, we propose a novel multi-channel framework for multi-talker separation. In the proposed model, an input multi-channel mixture signal is firstly converted to a set of beamformed signals using fixed beam patterns. For this beamforming, we propose to use differential beamformers as they are more suitable for speech separation. Then each beamformed signal is fed into a single-channel anchored deep attractor network to generate separated signals. And the final separation is acquired by post selecting the separating output for each beams. To evaluate the proposed system, we create a challenging dataset comprising mixtures of 2, 3 or 4 speakers. Our results show that the proposed system largely improves the state of the art in speech separation, achieving 11.5 dB, 11.76 dB and 11.02 dB average signal-to-distortion ratio improvement for 4, 3 and 2 overlapped speaker mixtures, which is comparable to the performance of a minimum variance distortionless response beamformer that uses oracle location, source, and noise information. We also run speech recognition with a clean trained acoustic model on the separated speech, achieving relative word error rate (WER) reduction of 45.76\%, 59.40\% and 62.80\% on fully overlapped speech of 4, 3 and 2 speakers, respectively. With a far talk acoustic model, the WER is further reduced.

Includes members:
  Member "speaker independent speech separation with deep attractor network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "supervised speaker embedding de mixing in two speaker environment")
  Member "deep attractor network for single microphone speaker separation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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")
  Member "large scale domain adaptation via teacher student learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fully learnable front end for multi channel acoustic modeling using semi supervised learning")
  Member "single channel multi speaker separation using deep clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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")
  Member "deep clustering discriminative embeddings for segmentation and separation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")
  Member "supervised and unsupervised speech enhancement using nonnegative matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "audio visual speech enhancement using conditional variational auto encoder")

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.SD(Sound)