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 monkeytyping solution to the youtube 8m video understanding challenge"
  Abstract: This article describes the final solution of team monkeytyping, who finished in second place in the YouTube-8M video understanding challenge. The dataset used in this challenge is a large-scale benchmark for multi-label video classification. We extend the work in [1] and propose several improvements for frame sequence modeling. We propose a network structure called Chaining that can better capture the interactions between labels. Also, we report our approaches in dealing with multi-scale information and attention pooling. In addition, We find that using the output of model ensemble as a side target in training can boost single model performance. We report our experiments in bagging, boosting, cascade, and stacking, and propose a stacking algorithm called attention weighted stacking. Our final submission is an ensemble that consists of 74 sub models, all of which are listed in the appendix.

Connected through hyperedge HE-1 (induced by source paper "bert for large scale video segment classification with test time augmentation"):
  with members:
    - "learning video representations using contrastive bidirectional transformer"
    - "videobert a joint model for video and language representation learning"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "non local netvlad encoding for video classification"
    - "constrained size tensorflow models for youtube 8m video understanding challenge"
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "attention based deep multiple instance learning"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "efficient video classification using fewer frames"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "non local netvlad encoding for video classification"
    - "constrained size tensorflow models for youtube 8m video understanding challenge"
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "knowledge distillation by on the fly native ensemble"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "attention guided answer distillation for machine reading comprehension"
    - "learnable pooling methods for video classification"

Connected through hyperedge HE-3 (induced by source paper "multi attention networks for temporal localization of video level labels"):
  with members:
    - "attention based deep multiple instance learning"
    - "learnable pooling with context gating for video classification"
    - "temporal modeling approaches for large scale youtube 8m video understanding"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "language modeling with gated convolutional networks"
    - "youtube 8m a large scale video classification benchmark"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-4 (induced by source paper "non local netvlad encoding for video classification"):
  with members:
    - "qiniu submission to activitynet challenge 2018"
    - "moments in time dataset one million videos for event understanding"
    - "fine grained video attractiveness prediction using multimodal deep learning on a large real world dataset"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "video re localization"
    - "reconstruction network for video captioning"
    - "averaging weights leads to wider optima and better generalization"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-5 (induced by source paper "training compact deep learning models for video classification using circulant matrices"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "compressing neural networks using the variational information bottleneck"
    - "learnable pooling with context gating for video classification"
    - "temporal modeling approaches for large scale youtube 8m video understanding"
    - "ternary neural networks with fine grained quantization"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "youtube 8m a large scale video classification benchmark"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-6 (induced by source paper "constrained size tensorflow models for youtube 8m video understanding challenge"):
  with members:
    - "aggregating frame level features for large scale video classification"
    - "learnable pooling with context gating for video classification"
    - "temporal modeling approaches for large scale youtube 8m video understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "youtube 8m a large scale video classification benchmark"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "delving deeper into convolutional networks for learning video representations"

Connected through hyperedge HE-7 (induced by source paper "deep multimodal learning an effective method for video classification"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "learnable pooling with context gating for video classification"
    - "the youtube 8m kaggle competition challenges and methods"
    - "temporal modeling approaches for large scale youtube 8m video understanding"
    - "truly multi modal youtube 8m video classification with video audio and text"
    - "youtube 8m a large scale video classification benchmark"
    - "beyond short snippets deep networks for video classification"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-8 (induced by source paper "label denoising with large ensembles of heterogeneous neural networks"):
  with members:
    - "improving deep learning using generic data augmentation"
    - "attention is all you need"
    - "learnable pooling with context gating for video classification"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "mixup beyond empirical risk minimization"
    - "learning deep networks from noisy labels with dropout regularization"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "youtube 8m a large scale video classification benchmark"

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)