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 "fosnet an end to end trainable deep neural network for scene recognition"):
  Abstract: Scene recognition is an image recognition problem aimed at predicting the category of the place at which the image is taken. In this paper, a new scene recognition method using the convolutional neural network (CNN) is proposed. The proposed method is based on the fusion of the object and the scene information in the given image and the CNN framework is named as FOS (fusion of object and scene) Net. In addition, a new loss named scene coherence loss (SCL) is developed to train the FOSNet and to improve the scene recognition performance. The proposed SCL is based on the unique traits of the scene that the 'sceneness' spreads and the scene class does not change all over the image. The proposed FOSNet was experimented with three most popular scene recognition datasets, and their state-of-the-art performance is obtained in two sets: 60.14% on Places 2 and 90.37% on MIT indoor 67. The second highest performance of 77.28% is obtained on SUN 397.

Includes members:
  Member "from volcano to toyshop adaptive discriminative region discovery for scene recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi scale discriminative region discovery for weakly supervised object localization")
  Member "partial convolution based padding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "who make drivers stop towards driver centric risk assessment risk object identification via causal inference")
  Member "hybrid cnn and dictionary based models for scene recognition and domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantic aware scene recognition")
  Member "a robust indoor scene recognition method based on sparse representation":
    also connected through other hyperedges:
      (none)
  Member "learnable pooling with context gating for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pic permutation invariant convolution for recognizing long range activities")
  Member "knowledge guided disambiguation for large scale scene classification with multi resolution cnns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge integration networks for action recognition")
  Member "squeeze and excitation networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "weakly supervised patchnets describing and aggregating local patches for scene recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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)