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 "a baseline for multi label image classification using an ensemble of deep convolutional neural networks"):
  Abstract: Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks. Although performance gains have been reported, the backbone deep models of the proposed approaches and the evaluation metrics employed in different works vary, making it difficult to compare each fairly. Moreover, due to the lack of properly investigated baselines, the advantage introduced by the proposed techniques are often ambiguous. To address these issues, we make a thorough investigation of the mainstream deep convolutional neural network architectures for multi-label image classification and present a strong baseline. With the use of proper data augmentation techniques and model ensembles, the basic deep architectures can achieve better performance than many existing more complex ones on three benchmark datasets, providing great insight for the future studies on multi-label image classification.

Includes members:
  Member "data augmentation by pairing samples for images classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "snapshot ensembles train 1 get m for free":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "multi label image recognition by recurrently discovering attentional regions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pedestrian attribute recognition a survey")
  Member "kill two birds with one stone weakly supervised neural network for image annotation and tag refinement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi label image classification via knowledge distillation from weakly supervised detection")
  Member "mixup beyond empirical risk minimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "multi label zero shot human action recognition via joint latent embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "zero shot action recognition in videos a survey")
  Member "learning spatial regularization with image level supervisions for multi label image classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images")

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)