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 speaker feature learning for text independent speaker verification"
  Abstract: Recently deep neural networks (DNNs) have been used to learn speaker features. However, the quality of the learned features is not sufficiently good, so a complex back-end model, either neural or probabilistic, has to be used to address the residual uncertainty when applied to speaker verification, just as with raw features. This paper presents a convolutional time-delay deep neural network structure (CT-DNN) for speaker feature learning. Our experimental results on the Fisher database demonstrated that this CT-DNN can produce high-quality speaker features: even with a single feature (0.3 seconds including the context), the EER can be as low as 7.68%. This effectively confirmed that the speaker trait is largely a deterministic short-time property rather than a long-time distributional pattern, and therefore can be extracted from just dozens of frames.

Connected through hyperedge HE-1 (induced by source paper "partial auc optimization based deep speaker embeddings with class center learning for text independent speaker verification"):
  with members:
    - "large margin softmax loss for speaker verification"
    - "deep speaker embedding learning with multi level pooling for text independent speaker verification"
    - "optimization of the area under the roc curve using neural network supervectors for text dependent speaker verification"
    - "voxceleb2 deep speaker recognition"
    - "voxceleb a large scale speaker identification dataset"
    - "deep speaker an end to end neural speaker embedding system"
    - "end to end text dependent speaker verification"

Connected through hyperedge HE-2 (induced by source paper "vae based regularization for deep speaker embedding"):
  with members:
    - "voxceleb a large scale speaker identification dataset"
    - "end to end attention based text dependent speaker verification"
    - "musan a music speech and noise corpus"
    - "end to end text dependent speaker verification"

Connected through hyperedge HE-3 (induced by source paper "speaker diarisation using 2d self attentive combination of embeddings"):
  with members:
    - "end to end attention based text dependent speaker verification"
    - "speaker diarization using deep recurrent convolutional neural networks for speaker embeddings"
    - "end to end text dependent speaker verification"

Connected through hyperedge HE-4 (induced by source paper "sample efficient adaptive text to speech"):
  with members:
    - "neural voice cloning with a few samples"
    - "playing hard exploration games by watching youtube"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "observe and look further achieving consistent performance on atari"
    - "fitting new speakers based on a short untranscribed sample"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "voiceloop voice fitting and synthesis via a phonological loop"

Connected through hyperedge HE-5 (induced by source paper "exploring a unified attention based pooling framework for speaker verification"):
  with members:
    - "attention is all you need"
    - "deep speaker an end to end neural speaker embedding system"
    - "end to end attention based text dependent speaker verification"
    - "effective approaches to attention based neural machine translation"
    - "end to end attention based large vocabulary speech recognition"
    - "show attend and tell neural image caption generation with visual attention"
    - "end to end text dependent speaker verification"

Connected through hyperedge HE-6 (induced by source paper "comparison of multiple features and modeling methods for text dependent speaker verification"):
  with members:
    - "deep speaker an end to end neural speaker embedding system"
    - "end to end attention based text dependent speaker verification"
    - "deep speaker verification do we need end to end"
    - "end to end text dependent speaker verification"

Connected through hyperedge HE-7 (induced by source paper "cross lingual speaker verification with deep feature learning"):
  with members:
    - "deep speaker an end to end neural speaker embedding system"
    - "end to end attention based text dependent speaker verification"
    - "parallel training of dnns with natural gradient and parameter averaging"

Connected through hyperedge HE-8 (induced by source paper "deep speaker verification do we need end to end"):
  with members:
    - "deep speaker an end to end neural speaker embedding system"
    - "end to end attention based text dependent speaker verification"

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)