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: "centroid based deep metric learning for speaker recognition"
  Abstract: Speaker embedding models that utilize neural networks to map utterances to a space where distances reflect similarity between speakers have driven recent progress in the speaker recognition task. However, there is still a significant performance gap between recognizing speakers in the training set and unseen speakers. The latter case corresponds to the few-shot learning task, where a trained model is evaluated on unseen classes. Here, we optimize a speaker embedding model with prototypical network loss (PNL), a state-of-the-art approach for the few-shot image classification task. The resulting embedding model outperforms the state-of-the-art triplet loss based models in both speaker verification and identification tasks, for both seen and unseen speakers.

Connected through hyperedge HE-1 (induced by source paper "video face clustering with unknown number of clusters"):
  with members:
    - "self supervised learning of face representations for video face clustering"
    - "learnable pins cross modal embeddings for person identity"
    - "tvqa localized compositional video question answering"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "generating descriptions with grounded and co referenced people"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "a theoretical analysis of the number of shots in few shot learning"):
  with members:
    - "video face clustering with unknown number of clusters"
    - "infinite mixture prototypes for few shot learning"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning with latent embedding optimization"
    - "learning embedding adaptation for few shot learning"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "prototypical networks for few shot 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.LG(Machine Learning)