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: "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
  Abstract: Five billion people in the world lack access to quality surgical care. Surgeon skill varies dramatically, and many surgical patients suffer complications and avoidable harm. Improving surgical training and feedback would help to reduce the rate of complications, half of which have been shown to be preventable. To do this, it is essential to assess operative skill, a process that currently requires experts and is manual, time consuming, and subjective. In this work, we introduce an approach to automatically assess surgeon performance by tracking and analyzing tool movements in surgical videos, leveraging region-based convolutional neural networks. In order to study this problem, we also introduce a new dataset, m2cai16-tool-locations, which extends the m2cai16-tool dataset with spatial bounds of tools. While previous methods have addressed tool presence detection, ours is the first to not only detect presence but also spatially localize surgical tools in real-world laparoscopic surgical videos. We show that our method both effectively detects the spatial bounds of tools as well as significantly outperforms existing methods on tool presence detection. We further demonstrate the ability of our method to assess surgical quality through analysis of tool usage patterns, movement range, and economy of motion.

Connected through hyperedge HE-1 (induced by source paper "a question centric model for visual question answering in medical imaging"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bach grand challenge on breast cancer histology images"
    - "concept centric visual turing tests for method validation"
    - "overcoming data limitation in medical visual question answering"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "visual question answering datasets algorithms and future challenges"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "manipulation skill assessment from videos with spatial attention network"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mutual context network for jointly estimating egocentric gaze and actions"
    - "boosted attention leveraging human attention for image captioning"
    - "bam bottleneck attention module"
    - "learning to score the figure skating sports videos"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "cbam convolutional block attention module"
    - "video and accelerometer based motion analysis for automated surgical skills assessment"

Connected through hyperedge HE-3 (induced by source paper "fast and accurate convolutional object detectors for real time embedded platforms"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep learning for generic object detection a survey"
    - "deformable convnets v2 more deformable better results"
    - "couplenet coupling global structure with local parts for object detection"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "video based surgical skill assessment using 3d convolutional neural networks"):
  with members:
    - "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "video and accelerometer based motion analysis for automated surgical skills assessment"
    - "temporal segment networks for action recognition in videos"
    - "automated surgical skill assessment in rmis training"
    - "who s better who s best pairwise deep ranking for skill determination"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-5 (induced by source paper "rasnet segmentation for tracking surgical instruments in surgical videos using refined attention segmentation network"):
  with members:
    - "2017 robotic instrument segmentation challenge"
    - "learning a discriminative feature network for semantic segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "pyramid attention network for semantic segmentation"
    - "squeeze and excitation networks"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (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"
    - "simultaneous recognition and pose estimation of instruments in minimally invasive surgery"
    - "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"

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)