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: "constrained size tensorflow models for youtube 8m video understanding challenge"
  Abstract: This paper presents our 7th place solution to the second YouTube-8M video understanding competition which challenges participates to build a constrained-size model to classify millions of YouTube videos into thousands of classes. Our final model consists of four single models aggregated into one tensorflow graph. For each single model, we use the same network architecture as in the winning solution of the first YouTube-8M video understanding competition, namely Gated NetVLAD. We train the single models separately in tensorflow's default float32 precision, then replace weights with float16 precision and ensemble them in the evaluation and inference stages., achieving 48.5% compression rate without loss of precision. Our best model achieved 88.324% GAP on private leaderboard. The code is publicly available at this https URL

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"
    - "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-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"
    - "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"

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)