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: "tristounet triplet loss for speaker turn embedding"
  Abstract: TristouNet is a neural network architecture based on Long Short-Term Memory recurrent networks, meant to project speech sequences into a fixed-dimensional euclidean space. Thanks to the triplet loss paradigm used for training, the resulting sequence embeddings can be compared directly with the euclidean distance, for speaker comparison purposes. Experiments on short (between 500ms and 5s) speech turn comparison and speaker change detection show that TristouNet brings significant improvements over the current state-of-the-art techniques for both tasks.

Connected through hyperedge HE-1 (induced by source paper "an open set recognition and few shot learning dataset for audio event classification in domestic environments"):
  with members:
    - "deep face recognition a survey"
    - "training neural audio classifiers with few data"
    - "ring loss convex feature normalization for face recognition"
    - "recent advances in open set recognition a survey"
    - "look listen and learn"
    - "audio scene classification with deep recurrent neural networks"
    - "icnet for real time semantic segmentation on high resolution images"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

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 speaker an end to end neural speaker embedding system"
    - "deep voice 3 2000 speaker neural text to speech"
    - "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 "unsupervised scalable representation learning for multivariate time series"):
  with members:
    - "deep learning for time series classification a review"
    - "a theoretical analysis of contrastive unsupervised representation learning"
    - "the uea multivariate time series classification archive 2018"
    - "random warping series a random features method for time series embedding"
    - "stochastic video generation with a learned prior"
    - "an efficient framework for learning sentence representations"
    - "som vae interpretable discrete representation learning on time series"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "improving collaborative metric learning with efficient negative sampling"):
  with members:
    - "deep content user embedding model for music recommendation"
    - "word2vec applied to recommendation hyperparameters matter"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "learning spread out local feature descriptors"
    - "how to train triplet networks with 100k identities"
    - "smart mining for deep metric learning"
    - "item2vec neural item embedding for collaborative filtering"

Connected through hyperedge HE-5 (induced by source paper "joint speech recognition and speaker diarization via sequence transduction"):
  with members:
    - "streaming end to end speech recognition for mobile devices"
    - "voxceleb2 deep speaker recognition"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "end to end text dependent speaker verification"
    - "adam a method for stochastic optimization"
    - "speech recognition with deep recurrent neural networks"
    - "sequence transduction with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "centroid based deep metric learning for speaker recognition"):
  with members:
    - "voxceleb2 deep speaker recognition"
    - "prototypical networks for few shot learning"
    - "deep speaker an end to end neural speaker embedding system"
    - "deep metric learning via facility location"
    - "facenet a unified embedding for face recognition and clustering"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "emotion detection using data driven models"):
  with members:
    - "deep net deep neural network for cyber security use cases"
    - "wassa 2017 shared task on emotion intensity"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-8 (induced by source paper "learning discriminative features using center loss and reconstruction as regularizer for speech emotion recognition"):
  with members:
    - "auxiliary tasks in multi task learning"
    - "emotion recognition from speech with recurrent neural networks"
    - "an overview of multi task learning in deep neural networks"

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)