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: "sound event detection using spatial features and convolutional recurrent neural network"
  Abstract: This paper proposes to use low-level spatial features extracted from multichannel audio for sound event detection. We extend the convolutional recurrent neural network to handle more than one type of these multichannel features by learning from each of them separately in the initial stages. We show that instead of concatenating the features of each channel into a single feature vector the network learns sound events in multichannel audio better when they are presented as separate layers of a volume. Using the proposed spatial features over monaural features on the same network gives an absolute F-score improvement of 6.1% on the publicly available TUT-SED 2016 dataset and 2.7% on the TUT-SED 2009 dataset that is fifteen times larger.

Connected through hyperedge HE-1 (induced by source paper "sound event detection of weakly labelled data with cnn transformer and automatic threshold optimization"):
  with members:
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "weakly labelled audioset tagging with attention neural networks"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "attention is all you need"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-2 (induced by source paper "an attention mechanism for musical instrument recognition"):
  with members:
    - "learning a deep convnet for multi label classification with partial labels"
    - "frame level instrument recognition by timbre and pitch"
    - "multitask learning for frame level instrument recognition"
    - "deep sets"
    - "extended playing techniques the next milestone in musical instrument recognition"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "attention is all you need"

Connected through hyperedge HE-3 (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"
    - "deep neural networks for multiple speaker detection and localization"
    - "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"
    - "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-4 (induced by source paper "language modelling for sound event detection with teacher forcing and scheduled sampling"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "audio surveillance a systematic review"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "weakly labeled sound event detection using tri training and adversarial learning"):
  with members:
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "audio event detection using weakly labeled data"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "gated feedback recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "adaptive pooling operators for weakly labeled sound event detection"):
  with members:
    - "sonyc a system for the monitoring analysis and mitigation of urban noise pollution"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "look listen and learn"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "a joint detection classification model for audio tagging of weakly labelled data"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised representation learning for unsynchronized audio visual events"):
  with members:
    - "objects that sound"
    - "surrey cvssp system for dcase2017 challenge task 4"
    - "see hear and read deep aligned representations"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "ambient sound provides supervision for visual learning"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "audio event detection using weakly labeled data"
    - "youtube 8m a large scale video classification benchmark"

Connected through hyperedge HE-8 (induced by source paper "identify locate and separate audio visual object extraction in large video collections using weak supervision"):
  with members:
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual event localization in unconstrained videos"
    - "learning to separate object sounds by watching unlabeled video"
    - "the sound of pixels"
    - "weakly supervised representation learning for unsynchronized audio visual events"
    - "the kinetics human action video dataset"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "cnn architectures for large scale audio classification"

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)