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: "musan a music speech and noise corpus"
  Abstract: This report introduces a new corpus of music, speech, and noise. This dataset is suitable for training models for voice activity detection (VAD) and music/speech discrimination. Our corpus is released under a flexible Creative Commons license. The dataset consists of music from several genres, speech from twelve languages, and a wide assortment of technical and non-technical noises. We demonstrate use of this corpus for music/speech discrimination on Broadcast news and VAD for speaker identification.

Connected through hyperedge HE-1 (induced by source paper "an end to end approach for the verification problem learning the right distance"):
  with members:
    - "an inductive bias for distances neural nets that respect the triangle inequality"
    - "contrastive multiview coding"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "voxceleb2 deep speaker recognition"
    - "additive margin softmax for face verification"
    - "learning non metric visual similarity for image retrieval"
    - "representation learning with contrastive predictive coding"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-2 (induced by source paper "robust speaker recognition using speech enhancement and attention model"):
  with members:
    - "audio visual speech enhancement using conditional variational auto encoder"
    - "self multi head attention for speaker recognition"
    - "speech enhancement with variational autoencoders and alpha stable distributions"
    - "attention mechanism in speaker recognition what does it learn in deep speaker embedding"
    - "attention based fully convolutional network for speech emotion recognition"
    - "cbam convolutional block attention module"
    - "voxceleb a large scale speaker identification dataset"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (induced by source paper "limitations of weak labels for embedding and tagging"):
  with members:
    - "learning problem agnostic speech representations from multiple self supervised tasks"
    - "massively parallel hyperparameter tuning"
    - "a closer look at weak label learning for audio events"
    - "training neural audio classifiers with few data"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "prototypical networks for few shot learning"
    - "learning from between class examples for deep sound recognition"

Connected through hyperedge HE-4 (induced by source paper "kite automatic speech recognition for unmanned aerial vehicles"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "vision meets drones a challenge"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "using the output embedding to improve language models"
    - "vqa visual question answering"

Connected through hyperedge HE-5 (induced by source paper "cochleanet a robust language independent audio visual model for speech enhancement"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "sdr half baked or well done"
    - "dnn driven speaker independent audio visual mask estimation for speech separation"
    - "the sound of pixels"
    - "lip reading driven deep learning approach for speech enhancement"
    - "audio visual scene analysis with self supervised multisensory features"
    - "supervised speech separation based on deep learning an overview"
    - "language and noise transfer in speech enhancement generative adversarial network"

Connected through hyperedge HE-6 (induced by source paper "large margin softmax loss for speaker verification"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "rethinking feature distribution for loss functions in image classification"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "ring loss convex feature normalization for face recognition"
    - "voxceleb2 deep speaker recognition"
    - "additive margin softmax for face verification"
    - "how to improve your speaker embeddings extractor in generic toolkits"

Connected through hyperedge HE-7 (induced by source paper "deep speech enhancement for reverberated and noisy signals using wide residual networks"):
  with members:
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "convolutional recurrent neural networks for speech enhancement"
    - "segan speech enhancement generative adversarial network"
    - "decoupled weight decay regularization"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "a fully convolutional neural network for speech enhancement"
    - "wide residual networks"

Connected through hyperedge HE-8 (induced by source paper "delving into voxceleb environment invariant speaker recognition"):
  with members:
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "training multi task adversarial network for extracting noise robust speaker embedding"
    - "voxceleb2 deep speaker recognition"
    - "recursive whitening transformation for speaker recognition on language mismatched condition"
    - "adversarial discriminative domain adaptation"
    - "voxceleb a large scale speaker identification dataset"
    - "coupled generative adversarial networks"
    - "simultaneous deep transfer across domains and tasks"

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)