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 "multi task zero shot action recognition with prioritised data augmentation"):
  Abstract: Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing the conventional model training requirement of annotated examples for every category. This is achieved by establishing a mapping connecting low-level features and a semantic description of the label space, referred as visual-semantic mapping, on auxiliary data. Re-using the learned mapping to project target videos into an embedding space thus allows novel-classes to be recognised by nearest neighbour inference. However, existing ZSL methods suffer from auxiliary-target domain shift intrinsically induced by assuming the same mapping for the disjoint auxiliary and target classes. This compromises the generalisation accuracy of ZSL recognition on the target data. In this work, we improve the ability of ZSL to generalise across this domain shift in both model- and data-centric ways by formulating a visual-semantic mapping with better generalisation properties and a dynamic data re-weighting method to prioritise auxiliary data that are relevant to the target classes. Specifically: (1) We introduce a multi-task visual-semantic mapping to improve generalisation by constraining the semantic mapping parameters to lie on a low-dimensional manifold, (2) We explore prioritised data augmentation by expanding the pool of auxiliary data with additional instances weighted by relevance to the target domain. The proposed new model is applied to the challenging zero-shot action recognition problem to demonstrate its advantages over existing ZSL models.

Includes members:
  Member "dynamic concept composition for zero example event detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recent advances in zero shot recognition")
  Member "reasoning about linguistic regularities in word embeddings using matrix manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "calculated attributes of synonym sets")
  Member "transductive multi view zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "a robust and efficient video representation for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "short term temporal convolutional networks for dynamic hand gesture recognition")
  Member "evaluation of output embeddings for fine grained image classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "semantic embedding space for zero shot action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")
  Member "zero shot action recognition by word vector embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "zero shot visual recognition via bidirectional latent embedding")
  Member "transductive zero shot action recognition by word vector embedding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")

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)