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: "surgical activity recognition in robot assisted radical prostatectomy using deep learning"
  Abstract: Adverse surgical outcomes are costly to patients and hospitals. Approaches to benchmark surgical care are often limited to gross measures across the entire procedure despite the performance of particular tasks being largely responsible for undesirable outcomes. In order to produce metrics from tasks as opposed to the whole procedure, methods to recognize automatically individual surgical tasks are needed. In this paper, we propose several approaches to recognize surgical activities in robot-assisted minimally invasive surgery using deep learning. We collected a clinical dataset of 100 robot-assisted radical prostatectomies (RARP) with 12 tasks each and propose `RP-Net', a modified version of InceptionV3 model, for image based surgical activity recognition. We achieve an average precision of 80.9% and average recall of 76.7% across all tasks using RP-Net which out-performs all other RNN and CNN based models explored in this paper. Our results suggest that automatic surgical activity recognition during RARP is feasible and can be the foundation for advanced analytics.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "segmental spatio temporal cnns for fine grained action segmentation and classification"
    - "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"

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)