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 "multi source doa estimation through pattern recognition of the modal coherence of a reverberant soundfield"):
  Abstract: We propose a novel multi-source direction of arrival (DOA) estimation technique using a convolutional neural network algorithm which learns the modal coherence patterns of an incident soundfield through measured spherical harmonic coefficients. We train our model for individual time-frequency bins in the short-time Fourier transform spectrum by analyzing the unique snapshot of modal coherence for each desired direction. The proposed method is capable of estimating simultaneously active multiple sound sources on a 3D space using a single-source training scheme. This single-source training scheme reduces the training time and resource requirements as well as allows the reuse of the same trained model for different multi-source combinations. The method is evaluated against various simulated and practical noisy and reverberant environments with varying acoustic criteria and found to outperform the baseline methods in terms of DOA estimation accuracy. Furthermore, the proposed algorithm allows independent training of azimuth and elevation during a full DOA estimation over 3D space which significantly improves its training efficiency without affecting the overall estimation accuracy.

Includes members:
  Member "sound event localization and detection of overlapping sources using convolutional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sound event detection with depthwise separable and dilated convolutions")
  Member "indoor sound source localization with probabilistic neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "watch listen and tell multi modal weakly supervised dense event captioning")
  Member "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for audio signal processing")
  Member "sound source localization in a multipath environment using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for audio signal processing")
  Member "semi supervised source localization on multiple manifolds with distributed microphones":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relative transfer function inverse regression from low dimensional manifold")
  Member "tensorflow large scale machine learning on heterogeneous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "a volumetric srp with refinement step for sound source localization":
    also connected through other hyperedges:
      (none)
  Member "audio surveillance a systematic review":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection")

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)