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: "training neural audio classifiers with few data"
  Abstract: We investigate supervised learning strategies that improve the training of neural network audio classifiers on small annotated collections. In particular, we study whether (i) a naive regularization of the solution space, (ii) prototypical networks, (iii) transfer learning, or (iv) their combination, can foster deep learning models to better leverage a small amount of training examples. To this end, we evaluate (i-iv) for the tasks of acoustic event recognition and acoustic scene classification, considering from 1 to 100 labeled examples per class. Results indicate that transfer learning is a powerful strategy in such scenarios, but prototypical networks show promising results when one does not count with external or validation data.

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"
    - "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"
    - "tristounet triplet loss for speaker turn embedding"

Connected through hyperedge HE-2 (induced by source paper "limitations of weak labels for embedding and tagging"):
  with members:
    - "learning problem agnostic speech representations from multiple self supervised tasks"
    - "massively parallel hyperparameter tuning"
    - "a closer look at weak label learning for audio events"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "prototypical networks for few shot learning"
    - "learning from between class examples for deep sound recognition"
    - "learning from weak and noisy labels for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "prototypical metric transfer learning for continuous speech keyword spotting with limited training data"):
  with members:
    - "prototypical networks for few shot learning"
    - "transfer learning for speech recognition on a budget"
    - "honk a pytorch reimplementation of convolutional neural networks for keyword spotting"
    - "transfer learning for music classification and regression tasks"
    - "matching networks for one shot learning"
    - "deep residual learning for image recognition"
    - "deep speech scaling up end to end speech recognition"
    - "deep metric learning using triplet network"

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)