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 image to video adaptation and fusion networks for action recognition"):
  Abstract: Existing deep learning methods for action recognition in videos require a large number of labeled videos for training, which is labor-intensive and time-consuming. For the same action, the knowledge learned from different media types, e.g., videos and images, may be related and complementary. However, due to the domain shifts and heterogeneous feature representations between videos and images, the performance of classifiers trained on images may be dramatically degraded when directly deployed to videos. In this paper, we propose a novel method, named Deep Image-to-Video Adaptation and Fusion Networks (DIVAFN), to enhance action recognition in videos by transferring knowledge from images using video keyframes as a bridge. The DIVAFN is a unified deep learning model, which integrates domain-invariant representations learning and cross-modal feature fusion into a unified optimization framework. Specifically, we design an efficient cross-modal similarities metric to reduce the modality shift among images, keyframes and videos. Then, we adopt an autoencoder architecture, whose hidden layer is constrained to be the semantic representations of the action class names. In this way, when the autoencoder is adopted to project the learned features from different domains to the same space, more compact, informative and discriminative representations can be obtained. Finally, the concatenation of the learned semantic feature representations from these three autoencoders are used to train the classifier for action recognition in videos. Comprehensive experiments on four real-world datasets show that our method outperforms some state-of-the-art domain adaptation and action recognition methods.

Includes members:
  Member "hierarchically learned view invariant representations for cross view action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "synthetic humans for action recognition from unseen viewpoints")
  Member "combined static and motion features for deep networks based activity recognition in videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a context aware capsule network for multi label classification")
  Member "transferable feature representation for visible to infrared cross dataset human action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "global temporal representation based cnns for infrared action recognition")
  Member "global temporal representation based cnns for infrared action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchically learned view invariant representations for cross view action recognition")
  Member "deep cross media knowledge transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mhtn modal adversarial hybrid transfer network for cross modal retrieval")
  Member "exploiting images for video recognition with hierarchical generative adversarial networks":
    also connected through other hyperedges:
      (none)
  Member "visual domain adaptation with manifold embedded distribution alignment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "impact of imagenet model selection on domain adaptation")
  Member "semantic autoencoder for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "crl class representative learning for image classification")

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)