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: "pyroomacoustics a python package for audio room simulation and array processing algorithms"
  Abstract: We present pyroomacoustics, a software package aimed at the rapid development and testing of audio array processing algorithms. The content of the package can be divided into three main components: an intuitive Python object-oriented interface to quickly construct different simulation scenarios involving multiple sound sources and microphones in 2D and 3D rooms; a fast C implementation of the image source model for general polyhedral rooms to efficiently generate room impulse responses and simulate the propagation between sources and receivers; and finally, reference implementations of popular algorithms for beamforming, direction finding, and adaptive filtering. Together, they form a package with the potential to speed up the time to market of new algorithms by significantly reducing the implementation overhead in the performance evaluation step.

Connected through hyperedge HE-1 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks"):
  with members:
    - "an optimized recurrent unit for ultra low power keyword spotting"
    - "streaming end to end speech recognition for mobile devices"
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic grammar augmentation for robust voice command recognition"
    - "spoken language understanding on the edge"
    - "sequence discriminative training for deep learning based acoustic keyword spotting"

Connected through hyperedge HE-2 (induced by source paper "adversarial music real world audio adversary against wake word detection system"):
  with members:
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "audio adversarial examples targeted attacks on speech to text"
    - "stress test evaluation for natural language inference"
    - "dolphinattack inaudible voice commands"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "hotflip white box adversarial examples for text classification"

Connected through hyperedge HE-3 (induced by source paper "predicting detection filters for small footprint open vocabulary keyword spotting"):
  with members:
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic grammar augmentation for robust voice command recognition"
    - "spoken language understanding on the edge"
    - "deep residual learning for small footprint keyword spotting"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "hello edge keyword spotting on microcontrollers"

Connected through hyperedge HE-4 (induced by source paper "whamr noisy and reverberant single channel speech separation"):
  with members:
    - "furcanext end to end monaural speech separation with dynamic gated dilated temporal convolutional networks"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "wham extending speech separation to noisy environments"
    - "sdr half baked or well done"
    - "end to end monaural multi speaker asr system without pretraining"
    - "a purely end to end system for multi speaker speech recognition"
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "single channel multi speaker separation using deep clustering"

Connected through hyperedge HE-5 (induced by source paper "static visual spatial priors for doa estimation"):
  with members:
    - "futuremapping the computational structure of spatial ai systems"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "building machines that learn and think like people"
    - "musan a music speech and noise corpus"
    - "conditional random fields as recurrent neural networks"
    - "multi scale context aggregation by dilated convolutions"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-6 (induced by source paper "hi mia a far field text dependent speaker verification database and the baselines"):
  with members:
    - "voxceleb2 deep speaker recognition"
    - "aishell 2 transforming mandarin asr research into industrial scale"
    - "voxceleb a large scale speaker identification dataset"
    - "musan a music speech and noise corpus"

Connected through hyperedge HE-7 (induced by source paper "deep tensor factorization for spatially aware scene decomposition"):
  with members:
    - "unsupervised deep clustering for source separation direct learning from mixtures using spatial information"
    - "a neural network alternative to non negative audio models"

Connected through hyperedge HE-8 (induced by source paper "w net bf dnn based beamformer using joint training approach"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "u net convolutional networks for biomedical image segmentation"

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)