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: "neural sign language translation based on human keypoint estimation"
  Abstract: We propose a sign language translation system based on human keypoint estimation. It is well-known that many problems in the field of computer vision require a massive amount of dataset to train deep neural network models. The situation is even worse when it comes to the sign language translation problem as it is far more difficult to collect high-quality training data. In this paper, we introduce the KETI sign language dataset which consists of 11,578 videos of high resolution and quality. Considering the fact that each country has a different and unique sign language, the KETI sign language dataset can be the starting line for further research on the Korean sign language translation. #R##N#Using the KETI sign language dataset, we develop a neural network model for translating sign videos into natural language sentences by utilizing the human keypoints extracted from a face, hands, and body parts. The obtained human keypoint vector is normalized by the mean and standard deviation of the keypoints and used as input to our translation model based on the sequence-to-sequence architecture. As a result, we show that our approach is robust even when the size of the training data is not sufficient. Our translation model achieves 94.6% (60.6%, respectively) translation accuracy on the validation set (test set, respectively) for 105 sentences that can be used in emergency situations. We compare several types of our neural sign translation models based on different attention mechanisms in terms of classical metrics for measuring the translation performance.

Connected through hyperedge HE-1 (induced by source paper "transferring cross domain knowledge for video sign language recognition"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "word level deep sign language recognition from video a new large scale dataset and methods comparison"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "ms asl a large scale data set and benchmark for understanding american sign language"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "prototypical networks for few shot learning"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "squeeze and excitation networks"

Connected through hyperedge HE-2 (induced by source paper "neural sign language translation by learning tokenization"):
  with members:
    - "zero shot sign language recognition can textual data uncover sign languages"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "ms asl a large scale data set and benchmark for understanding american sign language"
    - "attention is all you need"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "word level deep sign language recognition from video a new large scale dataset and methods comparison"):
  with members:
    - "learning trajectory dependencies for human motion prediction"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "action agnostic human pose forecasting"
    - "yolov3 an incremental improvement"
    - "ms asl a large scale data set and benchmark for understanding american sign language"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "on human motion prediction using recurrent neural networks"
    - "multi context attention for human pose estimation"

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)