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 "adversarial pyramid network for video domain generalization"):
  Abstract: This paper introduces a new research problem of video domain generalization (video DG) where most state-of-the-art action recognition networks degenerate due to the lack of exposure to the target domains of divergent distributions. While recent advances in video understanding focus on capturing the temporal relations of the long-term video context, we observe that the global temporal features are less generalizable in the video DG settings. The reason is that videos from other unseen domains may have unexpected absence, misalignment, or scale transformation of the temporal relations, which is known as the temporal domain shift. Therefore, the video DG is even more challenging than the image DG, which is also under-explored, because of the entanglement of the spatial and temporal domain shifts. #R##N#This finding has led us to view the key to video DG as how to effectively learn the local-relation features of different time scales that are more generalizable, and how to exploit them along with the global-relation features to maintain the discriminability. This paper presents the Adversarial Pyramid Network (APN), which captures the local-relation, global-relation, and multilayer cross-relation features progressively. This pyramid network not only improves the feature transferability from the view of representation learning, but also enhances the diversity and quality of the new data points that can bridge different domains when it is integrated with an improved version of the image DG adversarial data augmentation method. We construct four video DG benchmarks: UCF-HMDB, Something-Something, PKU-MMD, and NTU, in which the source and target domains are divided according to different datasets, different consequences of actions, or different camera views. The APN consistently outperforms previous action recognition models over all benchmarks.

Includes members:
  Member "temporal attentive alignment for video domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation")
  Member "feature critic networks for heterogeneous domain generalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting meta learning as supervised learning")
  Member "stm spatiotemporal and motion encoding for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi modal domain adaptation for fine grained action recognition")
  Member "domain generalization by solving jigsaw puzzles":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "domain adaptive ensemble learning")
  Member "spatiotemporal pyramid network for video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semantic image networks for human action recognition")
  Member "domain generalization via model agnostic learning of semantic features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain adaptive ensemble learning")
  Member "generalizing across domains via cross gradient training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "generalizing to unseen domains via adversarial data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cross domain few shot classification via learned feature wise transformation")

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)