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: "building a large concept bank for representing events in video"
  Abstract: Concept-based video representation has proven to be eective in complex event detection. However, existing methods either manually design concepts or directly adopt concept libraries not specically designed for events. In this paper, we propose to build Concept Bank, the largest concept library consisting of 4; 876 concepts specically designed to cover 631 real-world events. To construct the Concept Bank, we rst gather a comprehensive event collection from WikiHow, a collaborative writing project that aims to build the world’s largest manual for any possible How-To event. For each event, we then search Flickr and discover relevant concepts from the tags of the returned images. We train a Multiple Kernel Linear SVM for each discovered concept as a concept detector in Concept Bank. We organize the concepts into a ve-layer tree structure, in which the higher-level nodes correspond to the event categories while the leaf nodes are the event-specic concepts discovered for each event. Based on such tree ontology, we develop a semantic matching method to select relevant concepts for each textual event query, and then apply the corresponding concept detectors to generate concept-based video representations. We use TRECVID Multimedia Event Detection 2013 and Columbia Consumer Video open source event denitions and videos as our test sets and show very promising results on two video event detection tasks: event modeling over concept space and zero-shot event retrieval. To the best of our knowledge, this is the largest concept library covering the largest number of real-world events.

Connected through hyperedge HE-1 (induced by source paper "complex event recognition from images with few training examples"):
  with members:
    - "webly supervised learning of convolutional networks"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "automatic concept discovery from parallel text and visual corpora"
    - "selecting relevant web trained concepts for automated event retrieval"
    - "deep learning for single view instance recognition"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "conceptlearner discovering visual concepts from weakly labeled image collections"

Connected through hyperedge HE-2 (induced by source paper "vrfp on the fly video retrieval using web images and fast fisher vector products"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "sift meets cnn a decade survey of instance retrieval"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "selecting relevant web trained concepts for automated event retrieval"
    - "tagbook a semantic video representation without supervision for event detection"
    - "objects2action classifying and localizing actions without any video example"
    - "temporal localization of fine grained actions in videos by domain transfer from web images"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "eventnet a large scale structured concept library for complex event detection in video"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-4 (induced by source paper "selecting relevant web trained concepts for automated event retrieval"):
  with members:
    - "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.MM(Multimedia)