human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "hierarchy based image embeddings for semantic image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers")
  Member "tencent ml images a large scale multi label image database for visual representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering")
  Member "tafe net task aware feature embeddings for low shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automated relational meta learning")
  Member "large scale fine grained categorization and domain specific transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rethinking the hyperparameters for fine tuning")
  Member "bag of tricks for image classification with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "cosface large margin cosine loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "dada deep adversarial data augmentation for extremely low data regime classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "data augmentation with mobius transformations")
  Member "directional statistics based deep metric learning for image classification and retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "directional deep embedding and appearance learning for fast video object segmentation")

Question: Please tell me which class the source paper 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)