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 neural networks for multiple speaker detection and localization"
  Abstract: We propose to use neural networks for simultaneous detection and localization of multiple sound sources in human-robot interaction. In contrast to conventional signal processing techniques, neural network-based sound source localization methods require fewer strong assumptions about the environment. Previous neural network-based methods have been focusing on localizing a single sound source, which do not extend to multiple sources in terms of detection and localization. In this paper, we thus propose a likelihood-based encoding of the network output, which naturally allows the detection of an arbitrary number of sources. In addition, we investigate the use of sub-band cross-correlation information as features for better localization in sound mixtures, as well as three different network architectures based on different motivations. Experiments on real data recorded from a robot show that our proposed methods significantly outperform the popular spatial spectrum-based approaches.

Connected through hyperedge HE-1 (induced by source paper "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"):
  with members:
    - "multichannel sound event detection using 3d convolutional neural networks for learning inter channel features"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "a report on sound event detection with different binaural features"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "broadband doa estimation using convolutional neural networks trained with noise signals"
    - "sound source localization in a multipath environment using convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "polyphonic sound event detection and localization using a two stage strategy"):
  with members:
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "indoor sound source localization with probabilistic neural network"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "broadband doa estimation using convolutional neural networks trained with noise signals"
    - "sound source localization in a multipath environment using convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "localization detection and tracking of multiple moving sound sources with a convolutional recurrent neural network"):
  with members:
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "audio surveillance a systematic review"
    - "robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering"

Connected through hyperedge HE-4 (induced by source paper "mummer socially intelligent human robot interaction in public spaces"):
  with members:
    - "hierarchical multi task natural language understanding for cross domain conversational ai hermit nlu"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "viewing robot navigation in human environment as a cooperative activity"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-5 (induced by source paper "listening for sirens locating and classifying acoustic alarms in city scenes"):
  with members:
    - "aenet learning deep audio features for video analysis"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-6 (induced by source paper "diffraction aware sound localization for a non line of sight source"):
  with members:
    - "reflection aware sound source localization"

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)