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: "deep reinforcement learning for surgical gesture segmentation and classification"
  Abstract: Recognition of surgical gesture is crucial for surgical skill assessment and efficient surgery training. Prior works on this task are based on either variant graphical models such as HMMs and CRFs, or deep learning models such as Recurrent Neural Networks and Temporal Convolutional Networks. Most of the current approaches usually suffer from over-segmentation and therefore low segment-level edit scores. In contrast, we present an essentially different methodology by modeling the task as a sequential decision-making process. An intelligent agent is trained using reinforcement learning with hierarchical features from a deep model. Temporal consistency is integrated into our action design and reward mechanism to reduce over-segmentation errors. Experiments on JIGSAWS dataset demonstrate that the proposed method performs better than state-of-the-art methods in terms of the edit score and on par in frame-wise accuracy. Our code will be released later.

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"
    - "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 "automatic gesture recognition in robot assisted surgery with reinforcement learning and tree search"):
  with members:
    - "weakly supervised recognition of surgical gestures"
    - "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"
    - "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-3 (induced by source paper "reinforcement learning in healthcare a survey"):
  with members:
    - "does the artificial intelligence clinician learn optimal treatment strategies for sepsis in intensive care"
    - "a dual mode adaptive basal bolus advisor based on reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "improving sepsis treatment strategies by combining deep and kernel based reinforcement learning"
    - "partial policy based reinforcement learning for anatomical landmark localization in 3d medical images"
    - "automatic view planning with multi scale deep reinforcement learning agents"
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"
    - "deep reinforcement learning for dynamic treatment regimes on medical registry data"

Connected through hyperedge HE-4 (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"
    - "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"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning for imbalanced classification"):
  with members:
    - "imbalanced deep learning by minority class incremental rectification"
    - "reinforcement learning for relation classification from noisy data"
    - "deep reinforcement learning for list wise recommendations"
    - "focal loss for dense object detection"
    - "classification with costly features using deep reinforcement learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "should we really use post hoc tests based on mean ranks"
    - "cost sensitive learning of deep feature representations from imbalanced data"

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:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "recognizing surgical activities with recurrent neural networks"
    - "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"

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)