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: "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
  Abstract: This paper introduces a fast and efficient network architecture, NeXtVLAD, to aggregate frame-level features into a compact feature vector for large-scale video classification. Briefly speaking, the basic idea is to decompose a high-dimensional feature into a group of relatively low-dimensional vectors with attention before applying NetVLAD aggregation over time. This NeXtVLAD approach turns out to be both effective and parameter efficient in aggregating temporal information. In the 2nd Youtube-8M video understanding challenge, a single NeXtVLAD model with less than 80M parameters achieves a GAP score of 0.87846 in private leaderboard. A mixture of 3 NeXtVLAD models results in 0.88722, which is ranked 3rd over 394 teams. The code is publicly available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "multi label classification with label graph superimposing"):
  with members:
    - "centrality graph convolutional networks for skeleton based action recognition"
    - "multi label image recognition with graph convolutional networks"
    - "long term feature banks for detailed video understanding"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "a baseline for multi label image classification using an ensemble of deep convolutional neural networks"

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"
    - "non local netvlad encoding for video classification"
    - "constrained size tensorflow models for youtube 8m video understanding challenge"
    - "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"
    - "non local netvlad encoding for video classification"
    - "constrained size tensorflow models for youtube 8m video understanding challenge"
    - "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 "ensemblenet end to end optimization of multi headed models"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "deep mixture of experts via shallow embedding"
    - "large scale distributed neural network training through online distillation"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "slowfast networks for video recognition"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep mutual learning"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"

Connected through hyperedge HE-5 (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"
    - "non local netvlad encoding for 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-6 (induced by source paper "mod a deep mixture model with online knowledge distillation for large scale video temporal concept localization"):
  with members:
    - "non local netvlad encoding for 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-7 (induced by source paper "employ multimodal machine learning for content quality analysis"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "youtube 8m a large scale video classification benchmark"
    - "netvlad cnn architecture for weakly supervised place recognition"

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)