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: "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
  Abstract: Computer-assisted surgery (CAS) aims to provide the surgeon with the right type of assistance at the right moment. Such assistance systems are especially relevant in laparoscopic surgery, where CAS can alleviate some of the drawbacks that surgeons incur. For many assistance functions, e.g. displaying the location of a tumor at the appropriate time or suggesting what instruments to prepare next, analyzing the surgical workflow is a prerequisite. Since laparoscopic interventions are performed via endoscope, the video signal is an obvious sensor modality to rely on for workflow analysis. #R##N#Image-based workflow analysis tasks in laparoscopy, such as phase recognition, skill assessment, video indexing or automatic annotation, require a temporal distinction between video frames. Generally computer vision based methods that generalize from previously seen data are used. For training such methods, large amounts of annotated data are necessary. Annotating surgical data requires expert knowledge, therefore collecting a sufficient amount of data is difficult, time-consuming and not always feasible. #R##N#In this paper, we address this problem by presenting an unsupervised method for training a convolutional neural network (CNN) to differentiate between laparoscopic video frames on a temporal basis. We extract video frames at regular intervals from 324 unlabeled laparoscopic interventions, resulting in a dataset of approximately 2.2 million images. From this dataset, we extract image pairs from the same video and train a CNN to determine their temporal order. To solve this problem, the CNN has to extract features that are relevant for comprehending laparoscopic workflow. #R##N#Furthermore, we demonstrate that such a CNN can be adapted for surgical workflow segmentation. We performed image-based workflow segmentation on a publicly available dataset of 7 cholecystectomies and 9 colorectal interventions.

Connected through hyperedge HE-1 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction"):
  with members:
    - "using 3d convolutional neural networks to learn spatiotemporal features for automatic surgical gesture recognition in video"
    - "weakly supervised recognition of surgical gestures"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding"
    - "joint surgical gesture and task classification with multi task and multimodal learning"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "surgical data science for next generation interventions"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"

Connected through hyperedge HE-2 (induced by source paper "automated surgical activity recognition with one labeled sequence"):
  with members:
    - "learning from a tiny dataset of manual annotations a teacher student approach for surgical phase recognition"
    - "unsupervised learning for surgical motion by learning to predict the future"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "recognizing surgical activities with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning from a tiny dataset of manual annotations a teacher student approach for surgical phase recognition"):
  with members:
    - "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations"
    - "temporal coherence based self supervised learning for laparoscopic workflow analysis"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "data distillation towards omni supervised learning"
    - "single and multi task architectures for tool presence detection challenge at m2cai 2016"
    - "identity mappings in deep residual networks"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-4 (induced by source paper "active learning using deep bayesian networks for surgical workflow analysis"):
  with members:
    - "temporal coherence based self supervised learning for laparoscopic workflow analysis"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "deepphase surgical phase recognition in cataracts videos"
    - "on calibration of modern neural networks"
    - "exploiting the potential of unlabeled endoscopic video data with self supervised learning"
    - "deep bayesian active learning with image data"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "temporal coherence based self supervised learning for laparoscopic workflow analysis"):
  with members:
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "unsupervised representation learning by sorting sequences"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"
    - "slow and steady feature analysis higher order temporal coherence in video"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "prediction of laparoscopic procedure duration using unlabeled multimodal sensor data"):
  with members:
    - "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-7 (induced by source paper "exploiting the potential of unlabeled endoscopic video data with self supervised learning"):
  with members:
    - "clickstream analysis for crowd based object segmentation with confidence"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "semi supervised deep learning for fully convolutional networks"
    - "colorization as a proxy task for visual understanding"
    - "surgical data science for next generation interventions"
    - "understanding the mechanisms of deep transfer learning for medical images"
    - "deep residual learning for instrument segmentation in robotic surgery"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"

Connected through hyperedge HE-8 (induced by source paper "monitoring tool usage in cataract surgery videos using boosted convolutional and recurrent neural networks"):
  with members:
    - "deep image mining for diabetic retinopathy screening"
    - "learning transferable architectures for scalable image recognition"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "m2cai workflow challenge convolutional neural networks with time smoothing and hidden markov model for video frames classification"
    - "tool and phase recognition using contextual cnn features"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "single and multi task architectures for surgical workflow challenge at m2cai 2016"

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)