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 "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.

Includes members:
  Member "recognizing surgical activities with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction")
  Member "endonet a deep architecture for recognition tasks on laparoscopic videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction")
  Member "unsupervised visual representation learning by context prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  Member "on the properties of neural machine translation encoder decoder approaches":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "empirical evaluation of gated recurrent neural networks on sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "rich feature hierarchies for accurate object detection and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)