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: "ms asl a large scale data set and benchmark for understanding american sign language"
  Abstract: Sign language recognition is a challenging and often underestimated problem comprising multi-modal articulators (handshape, orientation, movement, upper body and face) that integrate asynchronously on multiple streams. Learning powerful statistical models in such a scenario requires much data, particularly to apply recent advances of the field. However, labeled data is a scarce resource for sign language due to the enormous cost of transcribing these unwritten languages. #R##N#We propose the first real-life large-scale sign language data set comprising over 25,000 annotated videos, which we thoroughly evaluate with state-of-the-art methods from sign and related action recognition. Unlike the current state-of-the-art, the data set allows to investigate the generalization to unseen individuals (signer-independent test) in a realistic setting with over 200 signers. Previous work mostly deals with limited vocabulary tasks, while here, we cover a large class count of 1000 signs in challenging and unconstrained real-life recording conditions. We further propose I3D, known from video classifications, as a powerful and suitable architecture for sign language recognition, outperforming the current state-of-the-art by a large margin. The data set is publicly available to the community.

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"
    - "neural sign language translation based on human keypoint estimation"
    - "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"
    - "neural sign language translation based on human keypoint estimation"
    - "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 "feph an annotated facial expression dataset for the rwth phoenix weather 2014 dataset"):
  with members:
    - "a comprehensive survey on machine learning techniques and user authentication approaches for credit card fraud detection"

Connected through hyperedge HE-4 (induced by source paper "mmtm multimodal transfer module for cnn fusion"):
  with members:
    - "my lips are concealed audio visual speech enhancement through obstructions"
    - "mfas multimodal fusion architecture search"
    - "the conversation deep audio visual speech enhancement"
    - "centralnet a multilayer approach for multimodal fusion"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"

Connected through hyperedge HE-5 (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"
    - "neural sign language translation based on human keypoint estimation"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "sign language recognition generation and translation an interdisciplinary perspective"):
  with members:
    - "american sign language fingerspelling recognition in the wild"
    - "datasheets for datasets"
    - "video based sign language recognition without temporal segmentation"
    - "swift a signwriting improved fast transcriber"

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)