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: "non local netvlad encoding for video classification"
  Abstract: This paper describes our solution for the 2$^\text{nd}$ YouTube-8M video understanding challenge organized by Google AI. Unlike the video recognition benchmarks, such as Kinetics and Moments, the YouTube-8M challenge provides pre-extracted visual and audio features instead of raw videos. In this challenge, the submitted model is restricted to 1GB, which encourages participants focus on constructing one powerful single model rather than incorporating of the results from a bunch of models. Our system fuses six different sub-models into one single computational graph, which are categorized into three families. More specifically, the most effective family is the model with non-local operations following the NetVLAD encoding. The other two family models are Soft-BoF and GRU, respectively. In order to further boost single models performance, the model parameters of different checkpoints are averaged. Experimental results demonstrate that our proposed system can effectively perform the video classification task, achieving 0.88763 on the public test set and 0.88704 on the private set in terms of GAP@20, respectively. We finally ranked at the fourth place in the YouTube-8M video understanding challenge.

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"
    - "hierarchical video frame sequence representation with deep convolutional graph network"
    - "motion appearance co memory networks for video question answering"
    - "multimodal dual attention memory for video story question answering"

Connected through hyperedge HE-2 (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"
    - "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"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-3 (induced by source paper "efficient video classification using fewer frames"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "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"
    - "label denoising with large ensembles of heterogeneous neural networks"

Connected through hyperedge HE-4 (induced by source paper "combinational class activation maps for weakly supervised object localization"):
  with members:
    - "residual non local attention networks for image restoration"
    - "semantic matching by weakly supervised 2d point set registration"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "long term feature banks for detailed video understanding"
    - "self produced guidance for weakly supervised object localization"
    - "adversarial complementary learning for weakly supervised object localization"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "attention u net learning where to look for the pancreas"

Connected through hyperedge HE-5 (induced by source paper "cross class relevance learning for temporal concept localization"):
  with members:
    - "gaussian temporal awareness networks for action localization"
    - "label denoising with large ensembles of heterogeneous neural networks"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "learnable pooling with context gating for video classification"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-6 (induced by source paper "hallucinating optical flow features for video classification"):
  with members:
    - "spatio temporal video re localization by warp lstm"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "video re localization"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"

Connected through hyperedge HE-7 (induced by source paper "mod a deep mixture model with online knowledge distillation for large scale video temporal concept localization"):
  with members:
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "knowledge distillation by on the fly native ensemble"
    - "large scale distributed neural network training through online distillation"
    - "deep mutual learning"
    - "learning without forgetting"
    - "convolutional two stream network fusion for video action recognition"
    - "fusing multi stream deep networks for video classification"
    - "delving deeper into convolutional networks for learning video representations"

Connected through hyperedge HE-8 (induced by source paper "long term feature banks for detailed video understanding"):
  with members:
    - "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"
    - "non local neural 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)