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: "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.

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"
    - "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
    - "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 "future state predicting lstm for early surgery type recognition"):
  with members:
    - "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations"
    - "learning to generate long term future via hierarchical prediction"
    - "encouraging lstms to anticipate actions very early"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "temporal segment networks towards good practices for deep action recognition"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-3 (induced by source paper "temporal segmentation of surgical sub tasks through deep learning with multiple data sources"):
  with members:
    - "weakly supervised recognition of surgical gestures"
    - "learning from a tiny dataset of manual annotations a teacher student approach for surgical phase recognition"
    - "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding"
    - "surgical activity recognition in robot assisted radical prostatectomy using deep learning"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "recognizing surgical activities with recurrent neural networks"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "segmental spatiotemporal cnns for fine grained action segmentation"

Connected through hyperedge HE-4 (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"
    - "temporal coherence based self supervised learning for laparoscopic workflow analysis"
    - "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-5 (induced by source paper "multi task recurrent convolutional network with correlation loss for surgical video analysis"):
  with members:
    - "incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "uncertainty in multitask learning joint representations for probabilistic mr only radiotherapy planning"
    - "deepphase surgical phase recognition in cataracts videos"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"

Connected through hyperedge HE-6 (induced by source paper "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations"):
  with members:
    - "am i done predicting action progress in videos"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-7 (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"
    - "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
    - "recognizing surgical activities with recurrent neural networks"

Connected through hyperedge HE-8 (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"
    - "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"

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)