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 "temporal spatial neural filter direction informed end to end multi channel target speech separation"):
  Abstract: Target speech separation refers to extracting the target speaker's speech from mixed signals. Despite the recent advances in deep learning based close-talk speech separation, the applications to real-world are still an open issue. Two main challenges are the complex acoustic environment and the real-time processing requirement. To address these challenges, we propose a temporal-spatial neural filter, which directly estimates the target speech waveform from multi-speaker mixture in reverberant environments, assisted with directional information of the speaker(s). Firstly, against variations brought by complex environment, the key idea is to increase the acoustic representation completeness through the jointly modeling of temporal, spectral and spatial discriminability between the target and interference source. Specifically, temporal, spectral, spatial along with the designed directional features are integrated to create a joint acoustic representation. Secondly, to reduce the latency, we design a fully-convolutional autoencoder framework, which is purely end-to-end and single-pass. All the feature computation is implemented by the network layers and operations to speed up the separation procedure. Evaluation is conducted on simulated reverberant dataset WSJ0-2mix and WSJ0-3mix under speaker-independent scenario. Experimental results demonstrate that the proposed method outperforms state-of-the-art deep learning based multi-channel approaches with fewer parameters and faster processing speed. Furthermore, the proposed temporal-spatial neural filter can handle mixtures with varying and unknown number of speakers and exhibits persistent performance even when existing a direction estimation error. Codes and models will be released soon.

Includes members:
  Member "phasebook and friends leveraging discrete representations for source separation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep filtering signal extraction and reconstruction using complex time frequency filters")
  Member "phase aware speech enhancement with deep complex u net":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cgcnn complex gabor convolutional neural network on raw speech")
  Member "conv tasnet surpassing ideal time frequency magnitude masking for speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "sdr half baked or well done":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "looking to listen at the cocktail party a speaker independent audio visual model for speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "end to end speech separation with unfolded iterative phase reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "cracking the cocktail party problem by multi beam deep attractor network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "conv tasnet surpassing ideal time frequency magnitude masking for speech separation")
  Member "deep extractor network for target speaker recovery from single channel speech mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "audio query based music source separation")

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)