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 "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"):
  Abstract: Real-time algorithms for automatically recognizing surgical phases are needed to develop systems that can provide assistance to surgeons, enable better management of operating room (OR) resources and consequently improve safety within the OR. State-of-the-art surgical phase recognition algorithms using laparoscopic videos are based on fully supervised training. This limits their potential for widespread application, since creation of manual annotations is an expensive process considering the numerous types of existing surgeries and the vast amount of laparoscopic videos available. In this work, we propose a new self-supervised pre-training approach based on the prediction of remaining surgery duration (RSD) from laparoscopic videos. The RSD prediction task is used to pre-train a convolutional neural network (CNN) and long short-term memory (LSTM) network in an end-to-end manner. Our proposed approach utilizes all available data and reduces the reliance on annotated data, thereby facilitating the scaling up of surgical phase recognition algorithms to different kinds of surgeries. Additionally, we present EndoN2N, an end-to-end trained CNN-LSTM model for surgical phase recognition and evaluate the performance of our approach on a dataset of 120 Cholecystectomy laparoscopic videos (Cholec120). This work also presents the first systematic study of self-supervised pre-training approaches to understand the amount of annotations required for surgical phase recognition. Interestingly, the proposed RSD pre-training approach leads to performance improvement even when all the training data is manually annotated and outperforms the single pre-training approach for surgical phase recognition presently published in the literature. It is also observed that end-to-end training of CNN-LSTM networks boosts surgical phase recognition performance.

Includes members:
  Member "multi task self supervised visual learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "unsupervised representation learning by sorting sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning representations by predicting bags of visual words")
  Member "monitoring tool usage in cataract surgery videos using boosted convolutional and recurrent neural networks":
    also connected through other hyperedges:
      (none)
  Member "m2cai workflow challenge convolutional neural networks with time smoothing and hidden markov model for video frames classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi task recurrent convolutional network with correlation loss for surgical video analysis")
  Member "learning representations for automatic colorization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning representations by predicting bags of visual words")
  Member "endonet a deep architecture for recognition tasks on laparoscopic videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction")
  Member "deep predictive coding networks for video prediction and unsupervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "videoflow a conditional flow based model for stochastic video generation")
  Member "colorful image colorization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")

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)