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: "simultaneous recognition and pose estimation of instruments in minimally invasive surgery"
  Abstract: Detection of surgical instruments plays a key role in ensuring patient safety in minimally invasive surgery. In this paper, we present a novel method for 2D vision-based recognition and pose estimation of surgical instruments that generalizes to different surgical applications. At its core, we propose a novel scene model in order to simultaneously recognize multiple instruments as well as their parts. We use a Convolutional Neural Network architecture to embody our model and show that the cross-entropy loss is well suited to optimize its parameters which can be trained in an end-to-end fashion. An additional advantage of our approach is that instrument detection at test time is achieved while avoiding the need for scale-dependent sliding window evaluation. This allows our approach to be relatively parameter free at test time and shows good performance for both instrument detection and tracking. We show that our approach surpasses state-of-the-art results on in-vivo retinal microsurgery image data, as well as ex-vivo laparoscopic sequences.

Connected through hyperedge HE-1 (induced by source paper "super deep a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction"):
  with members:
    - "surfelwarp efficient non volumetric single view dynamic reconstruction"
    - "open sourced reinforcement learning environments for surgical robotics"
    - "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics"
    - "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
    - "augmented reality predictive displays to help mitigate the effects of delayed telesurgery"
    - "pyramid stereo matching network"
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-2 (induced by source paper "towards generalizable surgical activity recognition using spatial temporal graph convolutional networks"):
  with members:
    - "using 3d convolutional neural networks to learn spatiotemporal features for automatic surgical gesture recognition in video"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "joint surgical gesture and task classification with multi task and multimodal learning"
    - "recognizing surgical activities with recurrent neural networks"
    - "temporal convolutional networks a unified approach to action segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "2017 robotic instrument segmentation challenge"):
  with members:
    - "cfcm segmentation via coarse to fine context memory"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "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"
    - "deeper depth prediction with fully convolutional residual networks"
    - "holistically nested edge detection"

Connected through hyperedge HE-4 (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"
    - "weakly supervised learning for tool localization in laparoscopic videos"
    - "deepphase surgical phase recognition in cataracts videos"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised learning for tool localization in laparoscopic videos"):
  with members:
    - "incorporating network built in priors in weakly supervised semantic segmentation"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "two phase learning for weakly supervised object localization"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"

Connected through hyperedge HE-6 (induced by source paper "simultaneous segmentation and classification of bone surfaces from ultrasound using a multi feature guided cnn"):
  with members:
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "i3posnet instrument pose estimation from x ray in temporal bone surgery"):
  with members:
    - "planning nonlinear access paths for temporal bone surgery"

Connected through hyperedge HE-8 (induced by source paper "fast 5dof needle tracking in ioct"):
  with members:
    - "concurrent segmentation and localization for tracking of surgical instruments"

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)