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 "real time surgical tools recognition in total knee arthroplasty using deep neural networks"):
  Abstract: Total knee arthroplasty (TKA) is a commonly performed surgical procedure to mitigate knee pain and improve functions for people with knee arthritis. The procedure is complicated due to the different surgical tools used in the stages of surgery. The recognition of surgical tools in real-time can be a solution to simplify surgical procedures for the surgeon. Also, the presence and movement of tools in surgery are crucial information for the recognition of the operational phase and to identify the surgical workflow. Therefore, this research proposes the development of a real-time system for the recognition of surgical tools during surgery using a convolutional neural network (CNN). Surgeons wearing smart glasses can see essential information about tools during surgery that may reduce the complication of the procedures. To evaluate the performance of the proposed method, we calculated and compared the Mean Average Precision (MAP) with state-of-the-art methods which are fast R-CNN and deformable part models (DPM). We achieved 87.6% mAP which is better in comparison to the existing methods. With the additional improvements of our proposed method, it can be a future point of reference, also the baseline for operational phase recognition.

Includes members:
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "endonet a deep architecture for recognition tasks on laparoscopic videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction")
  Member "fast r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)