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: "video stream retrieval of unseen queries using semantic memory"
  Abstract: Retrieval of live, user-broadcast video streams is an under-addressed and increasingly relevant challenge. The on-line nature of the problem requires temporal evaluation and the unforeseeable scope of potential queries motivates an approach which can accommodate arbitrary search queries. To account for the breadth of possible queries, we adopt a no-example approach to query retrieval, which uses a query's semantic relatedness to pre-trained concept classifiers. To adapt to shifting video content, we propose memory pooling and memory welling methods that favor recent information over long past content. We identify two stream retrieval tasks, instantaneous retrieval at any particular time and continuous retrieval over a prolonged duration, and propose means for evaluating them. Three large scale video datasets are adapted to the challenge of stream retrieval. We report results for our search methods on the new stream retrieval tasks, as well as demonstrate their efficacy in a traditional, non-streaming video task.

Connected through hyperedge HE-1 (induced by source paper "unified embedding and metric learning for zero exemplar event detection"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "rank pooling for action recognition"
    - "dynamic concept composition for zero example event detection"
    - "places an image database for deep scene understanding"
    - "audio event detection using weakly labeled data"
    - "jointly modeling embedding and translation to bridge video and language"
    - "zero shot event detection by multimodal distributional semantic embedding of videos"
    - "eventnet a large scale structured concept library for complex event detection in video"

Connected through hyperedge HE-2 (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"
    - "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"
    - "videostory embeddings recognize events when examples are scarce"
    - "semantic embedding space for zero shot action recognition"

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.IR(Information Retrieval)