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: "temporal modeling approaches for large scale youtube 8m video understanding"
  Abstract: This paper describes our solution for the video recognition task of the Google Cloud and YouTube-8M Video Understanding Challenge that ranked the 3rd place. Because the challenge provides pre-extracted visual and audio features instead of the raw videos, we mainly investigate various temporal modeling approaches to aggregate the frame-level features for multi-label video recognition. Our system contains three major components: two-stream sequence model, fast-forward sequence model and temporal residual neural networks. Experiment results on the challenging Youtube-8M dataset demonstrate that our proposed temporal modeling approaches can significantly improve existing temporal modeling approaches in the large-scale video recognition tasks. To be noted, our fast-forward LSTM with a depth of 7 layers achieves 82.75% in term of GAP@20 on the Kaggle Public test set.

Connected through hyperedge HE-1 (induced by source paper "hierarchical conditional relation networks for video question answering"):
  with members:
    - "learning to reason with relational video representation for question answering"
    - "progressive attention memory network for movie story question answering"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "long term feature banks for detailed video understanding"
    - "bilinear attention networks"
    - "non local netvlad encoding for video classification"
    - "hierarchical video frame sequence representation with deep convolutional graph network"
    - "motion appearance co memory networks for video question answering"

Connected through hyperedge HE-2 (induced by source paper "learning efficient video representation with video shuffle networks"):
  with members:
    - "learning video representations from correspondence proposals"
    - "collaborative spatio temporal feature learning for video action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "videos as space time region graphs"
    - "representation flow for action recognition"
    - "slowfast networks for video recognition"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-3 (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-4 (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-5 (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"
    - "the monkeytyping solution to the youtube 8m video understanding challenge"
    - "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-6 (induced by source paper "videos as space time region graphs"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "segmentation aware convolutional networks using local attention masks"
    - "joint discovery of object states and manipulation actions"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "detecting and recognizing human object interactions"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"

Connected through hyperedge HE-7 (induced by source paper "long term feature banks for detailed video understanding"):
  with members:
    - "non local netvlad encoding for video classification"
    - "scaling egocentric vision the epic kitchens dataset"
    - "a better baseline for ava"
    - "actor centric relation network"
    - "videos as space time region graphs"
    - "object level visual reasoning in videos"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-8 (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"

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)