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 patch learning for weakly supervised object classification and discovery"):
  Abstract: Patch-level image representation is very important for object classification and detection, since it is robust to spatial transformation, scale variation, and cluttered background. Many existing methods usually require fine-grained supervisions (e.g., bounding-box annotations) to learn patch features, which requires a great effort to label images may limit their potential applications. In this paper, we propose to learn patch features via weak supervisions, i.e., only image-level supervisions. To achieve this goal, we treat images as bags and patches as instances to integrate the weakly supervised multiple instance learning constraints into deep neural networks. Also, our method integrates the traditional multiple stages of weakly supervised object classification and discovery into a unified deep convolutional neural network and optimizes the network in an end-to-end way. The network processes the two tasks object classification and discovery jointly, and shares hierarchical deep features. Through this jointly learning strategy, weakly supervised object classification and discovery are beneficial to each other. We test the proposed method on the challenging PASCAL VOC datasets. The results show that our method can obtain state-of-the-art performance on object classification, and very competitive results on object discovery, with faster testing speed than competitors.

Includes members:
  Member "bayesian joint topic modelling for weakly supervised object localisation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph representation learning for audio music genre classification")
  Member "hcp a flexible cnn framework for multi label image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images")
  Member "deep filter banks for texture recognition description and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "srm a style based recalibration module for convolutional neural networks")
  Member "fast r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "relaxed multiple instance svm with application to object discovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "min entropy latent model for weakly supervised object detection")
  Member "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object discovery with a copy pasting gan")
  Member "exploit bounding box annotations for multi label object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving label ranking ensembles using boosting techniques")
  Member "exemplar based deep discriminative and shareable feature learning for scene image classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scene parsing with integration of parametric and non parametric models")

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)