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: "recognizing surgical activities with recurrent neural networks"
  Abstract: We apply recurrent neural networks to the task of recognizing surgical activities from robot kinematics. Prior work in this area focuses on recognizing short, low-level activities, or gestures, and has been based on variants of hidden Markov models and conditional random fields. In contrast, we work on recognizing both gestures and longer, higher-level activites, or maneuvers, and we model the mapping from kinematics to gestures/maneuvers with recurrent neural networks. To our knowledge, we are the first to apply recurrent neural networks to this task. Using a single model and a single set of hyperparameters, we match state-of-the-art performance for gesture recognition and advance state-of-the-art performance for maneuver recognition, in terms of both accuracy and edit distance. Code is available at this https URL .

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"
    - "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"

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"
    - "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding"
    - "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"
    - "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"
    - "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding"
    - "deepphase surgical phase recognition in cataracts videos"
    - "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 "towards generalizable surgical activity recognition using spatial temporal graph convolutional networks"):
  with members:
    - "using 3d convolutional neural networks to learn spatiotemporal features for automatic surgical gesture recognition in video"
    - "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"
    - "temporal convolutional networks a unified approach to action segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "multi task recurrent convolutional network with correlation loss for surgical video analysis"):
  with members:
    - "incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "uncertainty in multitask learning joint representations for probabilistic mr only radiotherapy planning"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "deepphase surgical phase recognition in cataracts videos"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "fine grained recognition in the wild a multi task domain adaptation approach"

Connected through hyperedge HE-6 (induced by source paper "using 3d convolutional neural networks to learn spatiotemporal features for automatic surgical gesture recognition in video"):
  with members:
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "temporal convolutional networks for action segmentation and detection"
    - "temporal segment networks towards good practices for deep action recognition"
    - "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-7 (induced by source paper "desk a robotic activity dataset for dexterous surgical skills transfer to medical robots"):
  with members:
    - "unsupervised learning for surgical motion by learning to predict the future"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "sim to real robot learning from pixels with progressive nets"
    - "learning modular neural network policies for multi task and multi robot transfer"

Connected through hyperedge HE-8 (induced by source paper "novel evaluation of surgical activity recognition models using task based efficiency metrics"):
  with members:
    - "future state predicting lstm for early surgery type recognition"
    - "joint surgical gesture and task classification with multi task and multimodal learning"
    - "surgical activity recognition in robot assisted radical prostatectomy using deep learning"
    - "segmental spatio temporal cnns for fine grained action segmentation and classification"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "segmental spatiotemporal cnns for fine grained action segmentation"

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)