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: "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.

Connected through hyperedge HE-1 (induced by source paper "self supervised surgical tool segmentation using kinematic information"):
  with members:
    - "exploiting the potential of unlabeled endoscopic video data with self supervised learning"
    - "time contrastive networks self supervised learning from video"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "deep residual learning for instrument segmentation in robotic surgery"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "automatic tip detection of surgical instruments in biportal endoscopic spine surgery"):
  with members:
    - "laptool net a contextual detector of surgical tools in laparoscopic videos based on recurrent convolutional neural networks"
    - "weakly supervised convolutional lstm approach for tool tracking in laparoscopic videos"
    - "focal loss for dense object detection"
    - "a survey on deep learning in medical image analysis"
    - "yolo9000 better faster stronger"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised convolutional lstm approach for tool tracking in laparoscopic videos"):
  with members:
    - "weakly supervised instance segmentation using class peak response"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "deepphase surgical phase recognition in cataracts videos"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "simultaneous recognition and pose estimation of instruments in minimally invasive surgery"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "mobile video object detection with temporally aware feature maps"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"

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)