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 "weakly supervised patchnets describing and aggregating local patches for scene recognition"):
  Abstract: Traditional feature encoding scheme (e.g., Fisher vector) with local descriptors (e.g., SIFT) and recent convolutional neural networks (CNNs) are two classes of successful methods for image recognition. In this paper, we propose a hybrid representation, which leverages the discriminative capacity of CNNs and the simplicity of descriptor encoding schema for image recognition, with a focus on scene recognition. To this end, we make three main contributions from the following aspects. First, we propose a patch-level and end-to-end architecture to model the appearance of local patches, called  PatchNet . PatchNet is essentially a customized network trained in a weakly supervised manner, which uses the image-level supervision to guide the patch-level feature extraction. Second, we present a hybrid visual representation, called  VSAD , by utilizing the robust feature representations of PatchNet to describe local patches and exploiting the semantic probabilities of PatchNet to aggregate these local patches into a global representation. Third, based on the proposed VSAD representation, we propose a new state-of-the-art scene recognition approach, which achieves an excellent performance on two standard benchmarks: MIT Indoor67 (86.2%) and SUN397 (73.0%).

Includes members:
  Member "hybrid cnn and dictionary based models for scene recognition and domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic aware scene recognition")
  Member "knowledge guided disambiguation for large scale scene classification with multi resolution cnns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge integration networks for action recognition")
  Member "locally supervised deep hybrid model for scene recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scene based factored attention for image captioning")
  Member "transferring object scene convolutional neural networks for event recognition in still images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "knowledge guided disambiguation for large scale scene classification with multi resolution cnns")
  Member "learning contextual dependence with convolutional hierarchical recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scene recognition with prototype agnostic scene layout")
  Member "actionness estimation using hybrid fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "actions as moving points")
  Member "temporal segment networks towards good practices for deep action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "object scene convolutional neural networks for event recognition in images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "graph neural networks for image understanding based on multiple cues group emotion recognition and event recognition as use cases")

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)