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: "videostory embeddings recognize events when examples are scarce"
  Abstract: This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their annotations, we propose to learn the entire representation from freely available web videos and their descriptions using an embedding between video features and term vectors. In our proposed embedding, which we call VideoStory, the correlations between the terms are utilized to learn a more effective representation by optimizing a joint objective balancing descriptiveness and predictability.We show how learning the VideoStory using a multimodal predictability loss, including appearance, motion and audio features, results in a better predictable representation. We also propose a variant of VideoStory to recognize an event in video from just the important terms in a text query by introducing a term sensitive descriptiveness loss. Our experiments on three challenging collections of web videos from the NIST TRECVID Multimedia Event Detection and Columbia Consumer Videos datasets demonstrate: i) the advantages of VideoStory over representations using attributes or alternative embeddings, ii) the benefit of fusing video modalities by an embedding over common strategies, iii) the complementarity of term sensitive descriptiveness and multimodal predictability for event recognition without examples. By it abilities to improve predictability upon any underlying video feature while at the same time maximizing semantic descriptiveness, VideoStory leads to state-of-the-art accuracy for both few- and zero-example recognition of events in video.

Connected through hyperedge HE-1 (induced by source paper "timeception for complex action recognition"):
  with members:
    - "video time properties encoders and evaluation"
    - "what have we learned from deep representations for action recognition"
    - "videos as space time region graphs"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "recent advances in zero shot recognition"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "joint intermodal and intramodal label transfers for extremely rare or unseen classes"
    - "progressive neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "zero shot learning the good the bad and the ugly"
    - "dynamic concept composition for zero example event detection"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"

Connected through hyperedge HE-3 (induced by source paper "spatial aware object embeddings for zero shot localization and classification of actions"):
  with members:
    - "localizing actions from video labels and pseudo annotations"
    - "video stream retrieval of unseen queries using semantic memory"
    - "spot on action localization from pointly supervised proposals"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "multi task zero shot action recognition with prioritised data augmentation"
    - "yfcc100m the new data in multimedia research"
    - "learning attributes equals multi source domain generalization"
    - "semantic embedding space for zero shot action recognition"

Connected through hyperedge HE-4 (induced by source paper "recognizing and presenting the storytelling video structure with deep multimodal networks"):
  with members:
    - "a data driven approach for tag refinement and localization in web videos"
    - "a deep siamese network for scene detection in broadcast videos"
    - "understanding deep image representations by inverting them"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"
    - "imagenet large scale visual recognition challenge"
    - "visualizing and understanding convolutional networks"
    - "distributed representations of words and phrases and their compositionality"

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)