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 attention based text dependent speaker verification"
  Abstract: A new type of End-to-End system for text-dependent speaker verification is presented in this paper. Previously, using the phonetically discriminative/speaker discriminative DNNs as feature extractors for speaker verification has shown promising results. The extracted frame-level (DNN bottleneck, posterior or d-vector) features are equally weighted and aggregated to compute an utterance-level speaker representation (d-vector or i-vector). In this work we use speaker discriminative CNNs to extract the noise-robust frame-level features. These features are smartly combined to form an utterance-level speaker vector through an attention mechanism. The proposed attention model takes the speaker discriminative information and the phonetic information to learn the weights. The whole system, including the CNN and attention model, is joint optimized using an end-to-end criterion. The training algorithm imitates exactly the evaluation process --- directly mapping a test utterance and a few target speaker utterances into a single verification score. The algorithm can automatically select the most similar impostor for each target speaker to train the network. We demonstrated the effectiveness of the proposed end-to-end system on Windows $10$ "Hey Cortana" speaker verification task.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "self multi head attention for speaker recognition"):
  with members:
    - "universal transformers"
    - "on deep speaker embeddings for text independent speaker recognition"
    - "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"
    - "listen attend and spell"

Connected through hyperedge HE-4 (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"
    - "invariant representations for noisy speech recognition"

Connected through hyperedge HE-5 (induced by source paper "time contrastive learning based deep bottleneck features for text dependent speaker verification"):
  with members:
    - "end to end neural segmental models for speech recognition"
    - "adversarial network bottleneck features for noise robust speaker verification"
    - "sequence prediction with neural segmental models"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "end to end training approaches for discriminative segmental models"
    - "understanding deep learning requires rethinking generalization"
    - "incorporating pass phrase dependent background models for text dependent speaker verification"
    - "unsupervised feature learning from temporal data"

Connected through hyperedge HE-6 (induced by source paper "encrypted speech recognition using deep polynomial networks"):
  with members:
    - "english conversational telephone speech recognition by humans and machines"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fixed point quantization of deep convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the computational efficiency of training neural networks"

Connected through hyperedge HE-7 (induced by source paper "vae based regularization for deep speaker embedding"):
  with members:
    - "voxceleb a large scale speaker identification dataset"
    - "deep speaker feature learning for text independent speaker verification"
    - "musan a music speech and noise corpus"
    - "end to end text dependent speaker verification"

Connected through hyperedge HE-8 (induced by source paper "speaker diarisation using 2d self attentive combination of embeddings"):
  with members:
    - "speaker diarization using deep recurrent convolutional neural networks for speaker embeddings"
    - "deep speaker feature learning for text independent speaker verification"
    - "end to end 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.CL(Computation and Language)