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: "end to end text dependent speaker verification"
  Abstract: In this paper we present a data-driven, integrated approach to speaker verification, which maps a test utterance and a few reference utterances directly to a single score for verification and jointly optimizes the system's components using the same evaluation protocol and metric as at test time. Such an approach will result in simple and efficient systems, requiring little domain-specific knowledge and making few model assumptions. We implement the idea by formulating the problem as a single neural network architecture, including the estimation of a speaker model on only a few utterances, and evaluate it on our internal "Ok Google" benchmark for text-dependent speaker verification. The proposed approach appears to be very effective for big data applications like ours that require highly accurate, easy-to-maintain systems with a small footprint.

Connected through hyperedge HE-1 (induced by source paper "dropclass and dropadapt dropping classes for deep speaker representation learning"):
  with members:
    - "learning from less data a unified data subset selection and active learning framework for computer vision"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "deep speaker embedding learning with multi level pooling for text independent speaker verification"
    - "arcface additive angular margin loss for deep face recognition"
    - "voxceleb2 deep speaker recognition"
    - "additive margin softmax for face verification"
    - "a systematic study of the class imbalance problem in convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "biometric recognition using deep learning a survey"):
  with members:
    - "p2sgrad refined gradients for optimizing deep face models"
    - "thirdeye triplet based iris recognition without normalization"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "deepiris iris recognition using a deep learning approach"
    - "fingernet pushing the limits of fingerprint recognition using convolutional neural network"
    - "generating talking face landmarks from speech"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-3 (induced by source paper "contextual phonetic pretraining for end to end utterance level language and speaker recognition"):
  with members:
    - "audio linguistic embeddings for spoken sentences"
    - "analyzing deep cnn based utterance embeddings for acoustic model adaptation"
    - "speaker embedding extraction with phonetic information"
    - "deep contextualized word representations"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "voxceleb2 deep speaker recognition"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "end to end anchored speech recognition"):
  with members:
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "end to end contextual speech recognition using class language models and a token passing decoder"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "deep extractor network for target speaker recovery from single channel speech mixtures"
    - "state of the art speech recognition with sequence to sequence models"
    - "attention based models for speech recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "adversarial speaker verification"):
  with members:
    - "adversarial speaker adaptation"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "segan speech enhancement generative adversarial network"
    - "deep speaker an end to end neural speaker embedding system"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "end to end attention based text dependent speaker verification"

Connected through hyperedge HE-6 (induced by source paper "optimization of the area under the roc curve using neural network supervectors for text dependent speaker verification"):
  with members:
    - "on deep speaker embeddings for text independent speaker recognition"
    - "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"
    - "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-7 (induced by source paper "attention augmented end to end multi task learning for emotion prediction from speech"):
  with members:
    - "dynamic difficulty awareness training for continuous emotion prediction"
    - "learning audio sequence representations for acoustic event classification"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "tacotron towards end to end speech synthesis"
    - "an overview of multi task learning in deep neural networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "recurrent models of visual attention"

Connected through hyperedge HE-8 (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"
    - "deep speaker feature learning for text independent 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.LG(Machine Learning)