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 "multi label image classification via knowledge distillation from weakly supervised detection"):
  Abstract: Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs. The WSD model is the teacher model and the classification model is the student model. After this cross-task knowledge distillation, the performance of the classification model is significantly improved and the efficiency is maintained since the WSD model can be safely discarded in the test phase. Extensive experiments on two large-scale datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior performances over the state-of-the-art methods on both performance and efficiency.

Includes members:
  Member "multi label image recognition by recurrently discovering attentional regions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pedestrian attribute recognition a survey")
  Member "order free rnn with visual attention for multi label classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross modality attention with semantic graph embedding for multi label classification")
  Member "kill two birds with one stone weakly supervised neural network for image annotation and tag refinement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a baseline for multi label image classification using an ensemble of deep convolutional neural networks")
  Member "miml fcn multi instance multi label learning via fully convolutional networks with privileged information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hard pixel mining for depth privileged semantic segmentation")
  Member "learning deep latent spaces for multi label classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "elucidating image to set prediction an analysis of models losses and datasets")
  Member "recurrent attentional reinforcement learning for multi label image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tree structured policy based progressive reinforcement learning for temporally language grounding in video")
  Member "learning spatial regularization with image level supervisions for multi label image classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images")
  Member "automatic image annotation via label transfer in the semantic space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a cnn rnn framework for image annotation from visual cues and social network metadata")

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)