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: "on deep speaker embeddings for text independent speaker recognition"
  Abstract: We investigate deep neural network performance in the textindependent speaker recognition task. We demonstrate that using angular softmax activation at the last classification layer of a classification neural network instead of a simple softmax activation allows to train a more generalized discriminative speaker embedding extractor. Cosine similarity is an effective metric for speaker verification in this embedding space. We also address the problem of choosing an architecture for the extractor. We found that deep networks with residual frame level connections outperform wide but relatively shallow architectures. This paper also proposes several improvements for previous DNN-based extractor systems to increase the speaker recognition accuracy. We show that the discriminatively trained similarity metric learning approach outperforms the standard LDA-PLDA method as an embedding backend. The results obtained on Speakers in the Wild and NIST SRE 2016 evaluation sets demonstrate robustness of the proposed systems when dealing with close to real-life conditions.

Connected through hyperedge HE-1 (induced by source paper "deep speaker embeddings for far field speaker recognition on short utterances"):
  with members:
    - "stc speaker recognition systems for the voices from a distance challenge"
    - "softmax dissection towards understanding intra and inter class objective for embedding learning"
    - "voices obscured in complex environmental settings voices corpus"
    - "arcface additive angular margin loss for deep face recognition"
    - "voxceleb2 deep speaker recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "voxceleb a large scale speaker identification dataset"

Connected through hyperedge HE-2 (induced by source paper "self multi head attention for speaker recognition"):
  with members:
    - "universal transformers"
    - "voxceleb2 deep speaker recognition"
    - "how to improve your speaker embeddings extractor in generic toolkits"
    - "attention is all you need"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "full info training for deep speaker feature learning"
    - "end to end attention based text dependent speaker verification"
    - "listen attend and spell"

Connected through hyperedge HE-3 (induced by source paper "optimization of the area under the roc curve using neural network supervectors for text dependent speaker verification"):
  with members:
    - "differentiable supervector extraction for encoding speaker and phrase information in text dependent speaker verification"
    - "random erasing data augmentation"
    - "on residual cnn in text dependent speaker verification task"
    - "bayesian dark knowledge"
    - "end to end text dependent speaker verification"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "h vectors utterance level speaker embedding using a hierarchical attention model"):
  with members:
    - "attention mechanism in speaker recognition what does it learn in deep speaker embedding"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-5 (induced by source paper "deep speaker embedding learning with multi level pooling for text independent speaker verification"):
  with members:
    - "deep speaker an end to end neural speaker embedding system"
    - "full info training for deep speaker feature learning"
    - "end to end text dependent speaker verification"
    - "deep residual learning for image recognition"

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)