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: "temporal coherence based self supervised learning for laparoscopic workflow analysis"
  Abstract: In order to provide the right type of assistance at the right time, computer-assisted surgery systems need context awareness. To achieve this, methods for surgical workflow analysis are crucial. Currently, convolutional neural networks provide the best performance for video-based workflow analysis tasks. For training such networks, large amounts of annotated data are necessary. However, collecting a sufficient amount of data is often costly, time-consuming, and not always feasible. In this paper, we address this problem by presenting and comparing different approaches for self-supervised pretraining of neural networks on unlabeled laparoscopic videos using temporal coherence. We evaluate our pretrained networks on Cholec80, a publicly available dataset for surgical phase segmentation, on which a maximum \(F_1\) score of 84.6 was reached. Furthermore, we were able to achieve an increase of the \(F_1\) score of up to 10 points when compared to a non-pretrained neural network.

Connected through hyperedge HE-1 (induced by source paper "unsupervised temporal video segmentation as an auxiliary task for predicting the remaining surgery duration"):
  with members:
    - "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations"
    - "unsupervised learning of action classes with continuous temporal embedding"
    - "unsupervised learning and segmentation of complex activities from video"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "slow and steady feature analysis higher order temporal coherence in video"

Connected through hyperedge HE-2 (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"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
    - "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-3 (induced by source paper "active learning using deep bayesian networks for surgical workflow analysis"):
  with members:
    - "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"
    - "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
    - "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"

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)