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 "weakly supervised learning for tool localization in laparoscopic videos"):
  Abstract: Surgical tool localization is an essential task for the automatic analysis of endoscopic videos. In the literature, existing methods for tool localization, tracking and segmentation require training data that is fully annotated, thereby limiting the size of the datasets that can be used and the generalization of the approaches. In this work, we propose to circumvent the lack of annotated data with weak supervision. We propose a deep architecture, trained solely on image level annotations, that can be used for both tool presence detection and localization in surgical videos. Our architecture relies on a fully convolutional neural network, trained end-to-end, enabling us to localize surgical tools without explicit spatial annotations. We demonstrate the benefits of our approach on a large public dataset, Cholec80, which is fully annotated with binary tool presence information and of which 5 videos have been fully annotated with bounding boxes and tool centers for the evaluation.

Includes members:
  Member "incorporating network built in priors in weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "restricted deformable convolution based road scene semantic segmentation using surround view cameras")
  Member "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a question centric model for visual question answering in medical imaging")
  Member "simultaneous recognition and pose estimation of instruments in minimally invasive surgery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "super deep a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction")
  Member "two phase learning for weakly supervised object localization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "min entropy latent model for weakly supervised object detection")
  Member "concurrent segmentation and localization for tracking of surgical instruments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery")
  Member "constrained deep weak supervision for histopathology image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "non parametric uni modality constraints for deep ordinal classification")
  Member "toolnet holistically nested real time segmentation of robotic surgical tools":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery")
  Member "hide and seek forcing a network to be meticulous for weakly supervised object and action localization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on feature normalization and data augmentation")

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)