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 "fine grained classification of rowing teams"):
  Abstract: Fine-grained classification tasks such as identifying different breeds of dog are quite challenging as visual differences between categories is quite small and can be easily overwhelmed by external factors such as object pose, lighting, etc. This work focuses on the specific case of classifying rowing teams from various associations. Currently, the photos are taken at rowing competitions and are manually classified by a small set of members, in what is a painstaking process. To alleviate this, Deep learning models can be utilised as a faster method to classify the images. Recent studies show that localising the manually defined parts, and modelling based on these parts, improves on vanilla convolution models, so this work also investigates the detection of clothing attributes. The networks were trained and tested on a partially labelled data set mainly consisting of rowers from multiple associations. This paper resulted in the classification of up to ten rowing associations by using deep learning networks the smaller VGG network achieved 90.1\% accuracy whereas ResNet was limited to 87.20\%. Adding attention to the ResNet resulted into a drop of performance as only 78.10\% was achieved.

Includes members:
  Member "see better before looking closer weakly supervised data augmentation network for fine grained visual classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention convolutional binary neural tree for fine grained visual categorization")
  Member "fixing the train test resolution discrepancy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "attribute aware attention model for fine grained representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ghostnet more features from cheap operations")
  Member "automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "arbitrary oriented object detection with circular smooth label")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "residual attention network for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "overfitting in adversarially robust deep learning")

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.CV(Computer Vision and Pattern Recognition)