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 an end to end neural speaker embedding system"
  Abstract: We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated by Deep Speaker can be used for many tasks, including speaker identification, verification, and clustering. We experiment with ResCNN and GRU architectures to extract the acoustic features, then mean pool to produce utterance-level speaker embeddings, and train using triplet loss based on cosine similarity. Experiments on three distinct datasets suggest that Deep Speaker outperforms a DNN-based i-vector baseline. For example, Deep Speaker reduces the verification equal error rate by 50% (relatively) and improves the identification accuracy by 60% (relatively) on a text-independent dataset. We also present results that suggest adapting from a model trained with Mandarin can improve accuracy for English speaker recognition.

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 "speech2phone a multilingual and text independent speaker identification model"):
  with members:
    - "neural voice cloning with a few samples"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "deep voice 3 2000 speaker neural text to speech"
    - "tristounet triplet loss for speaker turn embedding"
    - "tf learn tensorflow s high level module for distributed machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "untangling in invariant speech recognition"):
  with members:
    - "analyzing learned representations of a deep asr performance prediction model"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "geodesics of learned representations"
    - "convergent learning do different neural networks learn the same representations"

Connected through hyperedge HE-4 (induced by source paper "shareable representations for search query understanding"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "transfer learning for sequences via learning to collocate"
    - "linguistic knowledge and transferability of contextual representations"
    - "unsupervised multilingual word embeddings"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a survey of word embeddings evaluation methods"

Connected through hyperedge HE-5 (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-6 (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-7 (induced by source paper "voice face cross modal matching and retrieval a benchmark"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "disjoint mapping network for cross modal matching of voices and faces"
    - "arcface additive angular margin loss for deep face recognition"
    - "learnable pins cross modal embeddings for person identity"
    - "voxceleb2 deep speaker recognition"
    - "on learning associations of faces and voices"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-8 (induced by source paper "a study on angular based embedding learning for text independent speaker verification"):
  with members:
    - "large margin softmax loss for speaker verification"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "voxceleb2 deep speaker recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep hyperspherical learning"

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)