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: "aggregating frame level features for large scale video classification"
  Abstract: This paper introduces the system we developed for the Google Cloud & YouTube-8M Video Understanding Challenge, which can be considered as a multi-label classification problem defined on top of the large scale YouTube-8M Dataset. We employ a large set of techniques to aggregate the provided frame-level feature representations and generate video-level predictions, including several variants of recurrent neural networks (RNN) and generalized VLAD. We also adopt several fusion strategies to explore the complementarity among the models. In terms of the official metric GAP@20 (global average precision at 20), our best fusion model attains 0.84198 on the public 50\% of test data and 0.84193 on the private 50\% of test data, ranking 4th out of 650 teams worldwide in the competition.

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 "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-3 (induced by source paper "constrained size tensorflow models for youtube 8m video understanding challenge"):
  with members:
    - "learnable pooling with context gating for video classification"
    - "the monkeytyping solution to the youtube 8m video understanding challenge"
    - "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-4 (induced by source paper "learnable pooling with context gating for video classification"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the monkeytyping solution to the youtube 8m video understanding challenge"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "squeeze and excitation networks"
    - "temporal modeling approaches for large scale youtube 8m video understanding"
    - "a bag of words equivalent recurrent neural network for action recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "end to end video classification with knowledge graphs"):
  with members:
    - "learnable pooling with context gating for video classification"
    - "truly multi modal youtube 8m video classification with video audio and text"
    - "node2vec scalable feature learning for networks"
    - "youtube 8m a large scale video classification benchmark"
    - "visual relationship detection with language priors"
    - "yfcc100m the new data in multimedia research"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)