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: "zero shot event detection by multimodal distributional semantic embedding of videos"
  Abstract: We propose a new zero-shot Event Detection method by Multi-modal Distributional Semantic embedding of videos. Our model embeds object and action concepts as well as other available modalities from videos into a distributional semantic space. To our knowledge, this is the first Zero-Shot event detection model that is built on top of distributional semantics and extends it in the following directions: (a) semantic embedding of multimodal information in videos (with focus on the visual modalities), (b) automatically determining relevance of concepts/attributes to a free text query, which could be useful for other applications, and (c) retrieving videos by free text event query (e.g., "changing a vehicle tire") based on their content. We embed videos into a distributional semantic space and then measure the similarity between videos and the event query in a free text form. We validated our method on the large TRECVID MED (Multimedia Event Detection) challenge. Using only the event title as a query, our method outperformed the state-of-the-art that uses big descriptions from 12.6% to 13.5% with MAP metric and 0.73 to 0.83 with ROC-AUC metric. It is also an order of magnitude faster.

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"
    - "video stream retrieval of unseen queries using semantic memory"
    - "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"
    - "eventnet a large scale structured concept library for complex event detection in video"

Connected through hyperedge HE-2 (induced by source paper "twitter100k a real world dataset for weakly supervised cross media retrieval"):
  with members:
    - "person re identification past present and future"
    - "compound rank k projections for bilinear analysis"
    - "sift meets cnn a decade survey of instance retrieval"
    - "learning deep structure preserving image text embeddings"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "selecting relevant web trained concepts for automated event retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "packing and padding coupled multi index for accurate image retrieval"

Connected through hyperedge HE-3 (induced by source paper "explainable ai beware of inmates running the asylum or how i learnt to stop worrying and love the social and behavioural sciences"):
  with members:
    - "opening the black box of deep neural networks via information"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "graying the black box understanding dqns"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "plan explicability and predictability for robot task planning"

Connected through hyperedge HE-4 (induced by source paper "learning joint feature adaptation for zero shot recognition"):
  with members:
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "fast zero shot image tagging"
    - "improving semantic embedding consistency by metric learning for zero shot classification"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"
    - "transductive multi view zero shot learning"
    - "zero shot learning via semantic similarity embedding"
    - "predicting deep zero shot convolutional neural networks using textual descriptions"

Connected through hyperedge HE-5 (induced by source paper "write a classifier predicting visual classifiers from unstructured text"):
  with members:
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "learning hypergraph regularized attribute predictors"
    - "show attend and tell neural image caption generation with visual attention"
    - "evaluation of output embeddings for fine grained image classification"
    - "order embeddings of images and language"
    - "very deep convolutional networks for large scale image recognition"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "caffe convolutional architecture for fast feature embedding"

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)