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 learning on small datasets without pre training using cosine loss"
  Abstract: Two things seem to be indisputable in the contemporary deep learning discourse: 1. The categorical cross-entropy loss after softmax activation is the method of choice for classification. 2. Training a CNN classifier from scratch on small datasets does not work well. In contrast to this, we show that the cosine loss function provides significantly better performance than cross-entropy on datasets with only a handful of samples per class. For example, the accuracy achieved on the CUB-200-2011 dataset without pre-training is by 30% higher than with the cross-entropy loss. Further experiments on four other popular datasets confirm our findings. Moreover, we show that the classification performance can be improved further by integrating prior knowledge in the form of class hierarchies, which is straightforward with the cosine loss.

Connected through hyperedge HE-1 (induced by source paper "hyperspherical prototype networks"):
  with members:
    - "prototypical metric transfer learning for continuous speech keyword spotting with limited training data"
    - "infinite mixture prototypes for few shot learning"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "decoupled networks"
    - "fix your classifier the marginal value of training the last weight layer"
    - "cosface large margin cosine loss for deep face recognition"
    - "robust classification with convolutional prototype learning"
    - "learning towards minimum hyperspherical energy"

Connected through hyperedge HE-2 (induced by source paper "transfer learning based detection of diabetic retinopathy from small dataset"):
  with members:
    - "deep learning vs human graders for classifying severity levels of diabetic retinopathy in a real world nationwide screening program"
    - "deep learning based computer aided diagnosis systems for diabetic retinopathy a survey"
    - "deep image mining for diabetic retinopathy screening"
    - "a survey on deep learning in medical image analysis"
    - "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"
    - "rethinking the inception architecture for computer vision"

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)