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: "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding"
  Abstract: Fine-grained action segmentation and recognition is an important yet challenging task. Given a long, untrimmed sequence of kinematic data, the task is to classify the action at each time frame and segment the time series into the correct sequence of actions. In this paper, we propose a novel framework that combines a temporal Conditional Random Field (CRF) model with a powerful frame-level representation based on discriminative sparse coding. We introduce an end-to-end algorithm for jointly learning the weights of the CRF model, which include action classification and action transition costs, as well as an overcomplete dictionary of mid-level action primitives. This results in a CRF model that is driven by sparse coding features obtained using a discriminative dictionary that is shared among different actions and adapted to the task of structured output learning. We evaluate our method on three surgical tasks using kinematic data from the JIGSAWS dataset, as well as on a food preparation task using accelerometer data from the 50 Salads dataset. Our results show that the proposed method performs on par or better than state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction"):
  with members:
    - "using 3d convolutional neural networks to learn spatiotemporal features for automatic surgical gesture recognition in video"
    - "weakly supervised recognition of surgical gestures"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "joint surgical gesture and task classification with multi task and multimodal learning"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
    - "surgical data science for next generation interventions"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"

Connected through hyperedge HE-2 (induced by source paper "temporal segmentation of surgical sub tasks through deep learning with multiple data sources"):
  with members:
    - "weakly supervised recognition of surgical gestures"
    - "learning from a tiny dataset of manual annotations a teacher student approach for surgical phase recognition"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "surgical activity recognition in robot assisted radical prostatectomy using deep learning"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "recognizing surgical activities with recurrent neural networks"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "segmental spatiotemporal cnns for fine grained action segmentation"

Connected through hyperedge HE-3 (induced by source paper "automatic gesture recognition in robot assisted surgery with reinforcement learning and tree search"):
  with members:
    - "weakly supervised recognition of surgical gestures"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "deepphase surgical phase recognition in cataracts videos"
    - "recognizing surgical activities with recurrent neural networks"
    - "temporal convolutional networks for action segmentation and detection"
    - "segmental spatiotemporal cnns for fine grained action segmentation"
    - "temporal convolutional networks a unified approach to action segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "spatio temporal pyramid graph convolutions for human action recognition and postural assessment"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "skeleton based action recognition of people handling objects"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "toward ergonomic risk prediction via segmentation of indoor object manipulation actions using spatiotemporal convolutional networks"
    - "unsupervised discovery of parts structure and dynamics"
    - "open set domain adaptation for image and action recognition"
    - "a comprehensive survey on graph neural networks"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised recognition of surgical gestures"):
  with members:
    - "multi level discovery of deep options"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "temporal convolutional networks a unified approach to action segmentation"

Connected through hyperedge HE-6 (induced by source paper "stacked spatio temporal graph convolutional networks for action segmentation"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "graph edge convolutional neural networks for skeleton based action recognition"
    - "videos as space time region graphs"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "the description length of deep learning models"
    - "rgcnn regularized graph cnn for point cloud segmentation"
    - "situation recognition with graph 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)