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 "thinking outside the pool active training image creation for relative attributes"):
  Abstract: Current wisdom suggests more labeled image data is always better, and obtaining labels is the bottleneck. Yet curating a pool of sufficiently diverse and informative images is itself a challenge. In particular, training image curation is problematic for fine-grained attributes, where the subtle visual differences of interest may be rare within traditional image sources. We propose an active image generation approach to address this issue. The main idea is to jointly learn the attribute ranking task while also learning to generate novel realistic image samples that will benefit that task. We introduce an end-to-end framework that dynamically "imagines" image pairs that would confuse the current model, presents them to human annotators for labeling, then improves the predictive model with the new examples. With results on two datasets, we show that by thinking outside the pool of real images, our approach gains generalization accuracy for challenging fine-grained attribute comparisons.

Includes members:
  Member "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learn to augment joint data augmentation and network optimization for text recognition")
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "generative adversarial active learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "baod budget aware object detection")
  Member "the effectiveness of data augmentation in image classification using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "active decision boundary annotation with deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning human objectives by evaluating hypothetical behavior")
  Member "adversarial transformation networks learning to generate adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "learning from synthetic humans":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")

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)