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: "using 3d convolutional neural networks to learn spatiotemporal features for automatic surgical gesture recognition in video"
  Abstract: Automatically recognizing surgical gestures is a crucial step towards a thorough understanding of surgical skill. Possible areas of application include automatic skill assessment, intra-operative monitoring of critical surgical steps, and semi-automation of surgical tasks. Solutions that rely only on the laparoscopic video and do not require additional sensor hardware are especially attractive as they can be implemented at low cost in many scenarios. However, surgical gesture recognition based only on video is a challenging problem that requires effective means to extract both visual and temporal information from the video. Previous approaches mainly rely on frame-wise feature extractors, either handcrafted or learned, which fail to capture the dynamics in surgical video. To address this issue, we propose to use a 3D Convolutional Neural Network (CNN) to learn spatiotemporal features from consecutive video frames. We evaluate our approach on recordings of robot-assisted suturing on a bench-top model, which are taken from the publicly available JIGSAWS dataset. Our approach achieves high frame-wise surgical gesture recognition accuracies of more than 84%, outperforming comparable models that either extract only spatial features or model spatial and low-level temporal information separately. For the first time, these results demonstrate the benefit of spatiotemporal CNNs for video-based surgical gesture recognition.

Connected through hyperedge HE-1 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction"):
  with members:
    - "weakly supervised recognition of surgical gestures"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding"
    - "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 "towards generalizable surgical activity recognition using spatial temporal graph convolutional networks"):
  with members:
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "joint surgical gesture and task classification with multi task and multimodal learning"
    - "simultaneous recognition and pose estimation of instruments in minimally invasive surgery"
    - "recognizing surgical activities with recurrent neural networks"
    - "temporal convolutional networks a unified approach to action segmentation"
    - "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)