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: "the imagenet shuffle reorganized pre training for video event detection"
  Abstract: This paper strives for video event detection using a representation learned from deep convolutional neural networks. Different from the leading approaches, who all learn from the 1,000 classes defined in the ImageNet Large Scale Visual Recognition Challenge, we investigate how to leverage the complete ImageNet hierarchy for pre-training deep networks. To deal with the problems of over-specific classes and classes with few images, we introduce a bottom-up and top-down approach for reorganization of the ImageNet hierarchy based on all its 21,814 classes and more than 14 million images. Experiments on the TRECVID Multimedia Event Detection 2013 and 2015 datasets show that video representations derived from the layers of a deep neural network pre-trained with our reorganized hierarchy i) improves over standard pre-training, ii) is complementary among different reorganizations, iii) maintains the benefits of fusion with other modalities, and v) leads to state-of-the-art event detection results. The reorganized hierarchies and their derived Caffe models are publicly available at http://tinyurl.com/imagenetshuffle.

Connected through hyperedge HE-1 (induced by source paper "icap interative image captioning with predictive text"):
  with members:
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "show control and tell a framework for generating controllable and grounded captions"
    - "annotating object instances with a polygon rnn"
    - "twin networks matching the future for sequence generation"
    - "self critical sequence training for image captioning"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-2 (induced by source paper "predicting visual features from text for image and video caption retrieval"):
  with members:
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "generative adversarial text to image synthesis"
    - "learning joint representations of videos and sentences with web image search"
    - "image captioning with deep bidirectional lstms"
    - "rnn fisher vectors for action recognition and image annotation"
    - "learning deep structure preserving image text embeddings"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "eventnet a large scale structured concept library for complex event detection in video"

Connected through hyperedge HE-3 (induced by source paper "few shot adaptation for multimedia semantic indexing"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "semantic autoencoder for zero shot learning"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "zero shot learning the good the bad and the ugly"
    - "learning a deep embedding model for zero shot learning"
    - "places an image database for deep scene understanding"
    - "synthesized classifiers for zero shot learning"

Connected through hyperedge HE-4 (induced by source paper "spatio temporal instance learning action tubes from class supervision"):
  with members:
    - "pointly supervised action localization"
    - "localizing actions from video labels and pseudo annotations"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "action tubelet detector for spatio temporal action localization"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "human action localization with sparse spatial supervision"
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "incremental tube construction for human action detection"

Connected through hyperedge HE-5 (induced by source paper "coco cn for cross lingual image tagging captioning and retrieval"):
  with members:
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "causally regularized learning with agnostic data selection bias"
    - "predicting visual features from text for image and video caption retrieval"
    - "imagination improves multimodal translation"
    - "using artificial tokens to control languages for multilingual image caption generation"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "image pivoting for learning multilingual multimodal representations"

Connected through hyperedge HE-6 (induced by source paper "learning finer class networks for universal representations"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "universal sentence encoder"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learning multiple visual domains with residual adapters"
    - "learning more universal representations for transfer learning"

Connected through hyperedge HE-7 (induced by source paper "the new modality emoji challenges in prediction anticipation and retrieval"):
  with members:
    - "through a gender lens learning usage patterns of emojis from large scale android users"
    - "a semantics based measure of emoji similarity"
    - "sentiment perception of readers and writers in emoji use"
    - "emojinet building a machine readable sense inventory for emoji"
    - "yfcc100m the new data in multimedia research"
    - "the new data and new challenges in multimedia research"
    - "going deeper with convolutions"
    - "zero shot learning by convex combination of semantic embeddings"

Connected through hyperedge HE-8 (induced by source paper "learning more universal representations for transfer learning"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "home a household multimodal environment"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "learning multiple visual domains with residual adapters"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "yolo9000 better faster stronger"

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)