human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction"):
  Abstract: Surgical gesture recognition is important for surgical data science and computer-aided intervention. Even with robotic kinematic information, automatically segmenting surgical steps presents numerous challenges because surgical demonstrations are characterized by high variability in style, duration and order of actions. In order to extract discriminative features from the kinematic signals and boost recognition accuracy, we propose a multi-task recurrent neural network for simultaneous recognition of surgical gestures and estimation of a novel formulation of surgical task progress. To show the effectiveness of the presented approach, we evaluate its application on the JIGSAWS dataset, that is currently the only publicly available dataset for surgical gesture recognition featuring robot kinematic data. We demonstrate that recognition performance improves in multi-task frameworks with progress estimation without any additional manual labelling and training.

Includes members:
  Member "using 3d convolutional neural networks to learn spatiotemporal features for automatic surgical gesture recognition in video":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards generalizable surgical activity recognition using spatial temporal graph convolutional networks")
  Member "weakly supervised recognition of surgical gestures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "temporal segmentation of surgical sub tasks through deep learning with multiple data sources")
  Member "deep reinforcement learning for surgical gesture segmentation and classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automatic gesture recognition in robot assisted surgery with reinforcement learning and tree search")
  Member "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "temporal segmentation of surgical sub tasks through deep learning with multiple data sources")
  Member "joint surgical gesture and task classification with multi task and multimodal learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards generalizable surgical activity recognition using spatial temporal graph convolutional networks")
  Member "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "future state predicting lstm for early surgery type recognition")
  Member "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automated surgical activity recognition with one labeled sequence")
  Member "surgical data science for next generation interventions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cadis cataract dataset for image segmentation")

Question: Please tell me which class the source paper 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)