human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "out of distribution detection for generalized zero shot action recognition"
  Abstract: Generalized zero-shot action recognition is a challenging problem, where the task is to recognize new action categories that are unavailable during the training stage, in addition to the seen action categories. Existing approaches suffer from the inherent bias of the learned classifier towards the seen action categories. As a consequence, unseen category samples are incorrectly classified as belonging to one of the seen action categories. In this paper, we set out to tackle this issue by arguing for a separate treatment of seen and unseen action categories in generalized zero-shot action recognition. We introduce an out-of-distribution detector that determines whether the video features belong to a seen or unseen action category. To train our out-of-distribution detector, video features for unseen action categories are synthesized using generative adversarial networks trained on seen action category features. To the best of our knowledge, we are the first to propose an out-of-distribution detector based GZSL framework for action recognition in videos. Experiments are performed on three action recognition datasets: Olympic Sports, HMDB51 and UCF101. For generalized zero-shot action recognition, our proposed approach outperforms the baseline (f-CLSWGAN) with absolute gains (in classification accuracy) of 7.0%, 3.4%, and 4.9%, respectively, on these datasets.

Connected through hyperedge HE-1 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "semantically aligned bias reducing zero shot learning"
    - "transferable contrastive network for generalized zero shot learning"
    - "feedback network for image super resolution"
    - "generative dual adversarial network for generalized zero shot learning"
    - "towards universal representation for unseen action recognition"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "multi modal cycle consistent generalized zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "gtnet generative transfer network for zero shot object detection"):
  with members:
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "generative dual adversarial network for generalized zero shot learning"
    - "zero shot object detection by hybrid region embedding"
    - "polarity loss for zero shot object detection"
    - "visual data synthesis via gan for zero shot video classification"
    - "semantic autoencoder for zero shot learning"
    - "feature generating networks for zero shot learning"
    - "learning a deep embedding model for zero shot learning"

Connected through hyperedge HE-3 (induced by source paper "dazsl dynamic attributes for zero shot learning"):
  with members:
    - "zero shot action recognition in videos a survey"
    - "action2vec a crossmodal embedding approach to action learning"
    - "informed democracy voting based novelty detection for action recognition"
    - "visual data synthesis via gan for zero shot video classification"
    - "tsm temporal shift module for efficient video understanding"
    - "cross modal and hierarchical modeling of video and text"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "generalized zero shot learning for action recognition with web scale video data"

Connected through hyperedge HE-4 (induced by source paper "guided weak supervision for action recognition with scarce data to assess skills of children with autism"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "temporal segment networks towards good practices for deep action recognition"
    - "objects2action classifying and localizing actions without any video example"
    - "zero shot recognition with unreliable attributes"
    - "long term recurrent convolutional networks for visual recognition and description"

Question: Please tell me which class the center node 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)