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: "dynamic concept composition for zero example event detection"
  Abstract: In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars. In principle, zero-shot learning makes it possible to train an event detection model based on the assumption that events (e.g. \emph{birthday party}) can be described by multiple mid-level semantic concepts (e.g. "blowing candle", "birthday cake"). Towards this goal, we first pre-train a bundle of concept classifiers using data from other sources. Then we evaluate the semantic correlation of each concept \wrt the event of interest and pick up the relevant concept classifiers, which are applied on all test videos to get multiple prediction score vectors. While most existing systems combine the predictions of the concept classifiers with fixed weights, we propose to learn the optimal weights of the concept classifiers for each testing video by exploring a set of online available videos with free-form text descriptions of their content. To validate the effectiveness of the proposed approach, we have conducted extensive experiments on the latest TRECVID MEDTest 2014, MEDTest 2013 and CCV dataset. The experimental results confirm the superiority of the proposed approach.

Connected through hyperedge HE-1 (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"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "matching networks for one shot learning"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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-3 (induced by source paper "multi task zero shot action recognition with prioritised data augmentation"):
  with members:
    - "reasoning about linguistic regularities in word embeddings using matrix manifolds"
    - "transductive multi view zero shot learning"
    - "a robust and efficient video representation for action recognition"
    - "evaluation of output embeddings for fine grained image classification"
    - "semantic embedding space for zero shot action recognition"
    - "zero shot action recognition by word vector embedding"
    - "transductive zero shot action recognition by word vector embedding"
    - "improving zero shot learning by mitigating the hubness problem"

Connected through hyperedge HE-4 (induced by source paper "a novel learning based frame pooling method for event detection"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "imagenet large scale visual recognition challenge"
    - "a discriminative cnn video representation for event detection"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-5 (induced by source paper "what is the best way for extracting meaningful attributes from pictures"):
  with members:
    - "determining the best attributes for surveillance video keywords generation"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "discovering attribute shades of meaning with the crowd"
    - "coherent multi sentence video description with variable level of detail"
    - "discovering discriminative cell attributes for hep 2 specimen image classification"

Connected through hyperedge HE-6 (induced by source paper "determining the best attributes for surveillance video keywords generation"):
  with members:
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "coherent multi sentence video description with variable level of detail"
    - "discovering discriminative cell attributes for hep 2 specimen image classification"

Connected through hyperedge HE-7 (induced by source paper "automatic and quantitative evaluation of attribute discovery methods"):
  with members:
    - "determining the best attributes for surveillance video keywords generation"
    - "discovering attribute shades of meaning with the crowd"
    - "discovering discriminative cell attributes for hep 2 specimen image classification"

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)