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: "video based sign language recognition without temporal segmentation"
  Abstract: Millions of hearing impaired people around the world routinely use some variants of sign languages to communicate, thus the automatic translation of a sign language is meaningful and important. Currently, there are two sub-problems in Sign Language Recognition (SLR), i.e., isolated SLR that recognizes word by word and continuous SLR that translates entire sentences. Existing continuous SLR methods typically utilize isolated SLRs as building blocks, with an extra layer of preprocessing (temporal segmentation) and another layer of post-processing (sentence synthesis). Unfortunately, temporal segmentation itself is non-trivial and inevitably propagates errors into subsequent steps. Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data. To address these challenges, we propose a novel continuous sign recognition framework, the Hierarchical Attention Network with Latent Space (LS-HAN), which eliminates the preprocessing of temporal segmentation. The proposed LS-HAN consists of three components: a two-stream Convolutional Neural Network (CNN) for video feature representation generation, a Latent Space (LS) for semantic gap bridging, and a Hierarchical Attention Network (HAN) for latent space based recognition. Experiments are carried out on two large scale datasets. Experimental results demonstrate the effectiveness of the proposed framework.

Connected through hyperedge HE-1 (induced by source paper "spatial temporal multi cue network for continuous sign language recognition"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "skeleton based gesture recognition using several fully connected layers with path signature features and temporal transformer module"
    - "simple baselines for human pose estimation and tracking"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "convolutional two stream network fusion for video action recognition"
    - "fast r cnn"
    - "sequence to sequence video to text"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "recognizing american sign language manual signs from rgb d videos"):
  with members:
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "temporal 3d convnets new architecture and transfer learning for video classification"
    - "the kinetics human action video dataset"
    - "rank pooling for action recognition"
    - "evaluation of deep learning based pose estimation for sign language recognition"
    - "beyond temporal pooling recurrence and temporal convolutions for gesture recognition in video"

Connected through hyperedge HE-3 (induced by source paper "fingerspelling recognition in the wild with iterative visual attention"):
  with members:
    - "american sign language fingerspelling recognition in the wild"
    - "multitask training with unlabeled data for end to end sign language fingerspelling recognition"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "diversified visual attention networks for fine grained object classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "lexicon free fingerspelling recognition from video data models and signer adaptation"
    - "signer independent fingerspelling recognition with deep neural network adaptation"

Connected through hyperedge HE-4 (induced by source paper "sf net structured feature network for continuous sign language recognition"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "reconstruction network for video captioning"
    - "slowfast networks for video recognition"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "jointly modeling embedding and translation to bridge video and language"
    - "effective approaches to attention based neural machine translation"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-5 (induced by source paper "factored latent dynamic conditional random fields for single and multi label sequence modeling"):
  with members:
    - "context model for pedestrian intention prediction using factored latent dynamic conditional random fields"
    - "embedded state latent conditional random fields for sequence labeling"
    - "multi attention recurrent network for human communication comprehension"
    - "multi task learning for sequence tagging an empirical study"
    - "lstm a search space odyssey"
    - "joint learning of correlated sequence labelling tasks using bidirectional recurrent neural networks"
    - "hcp a flexible cnn framework for multi label image classification"
    - "cnn rnn a unified framework for multi label image classification"

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"
    - "ms asl a large scale data set and benchmark for understanding american sign language"
    - "swift a signwriting improved fast transcriber"

Connected through hyperedge HE-7 (induced by source paper "ms asl a large scale data set and benchmark for understanding american sign language"):
  with members:
    - "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "convolutional pose machines"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "ssd single shot multibox detector"
    - "convolutional two stream network fusion for video action recognition"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-8 (induced by source paper "attention based temporal weighted convolutional neural network for action recognition"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "long term temporal convolutions for action recognition"
    - "effective approaches to attention based neural machine translation"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "p cnn pose based cnn features for action recognition"
    - "deep residual learning for image 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)