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: "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations"
  Abstract: Accurate surgery duration estimation is necessary for optimal OR planning, which plays an important role in patient comfort and safety as well as resource optimization. It is, however, challenging to preoperatively predict surgery duration since it varies significantly depending on the patient condition, surgeon skills, and intraoperative situation. In this paper, we propose a deep learning pipeline, referred to as RSDNet, which automatically estimates the remaining surgery duration (RSD) intraoperatively by using only visual information from laparoscopic videos. The previous state-of-the-art approaches for RSD prediction are dependent on manual annotation, whose generation requires expensive expert knowledge and is time-consuming, especially considering the numerous types of surgeries performed in a hospital and the large number of laparoscopic videos available. A crucial feature of RSDNet is that it does not depend on any manual annotation during training, making it easily scalable to many kinds of surgeries. The generalizability of our approach is demonstrated by testing the pipeline on two large datasets containing different types of surgeries: 120 cholecystectomy and 170 gastric bypass videos. The experimental results also show that the proposed network significantly outperforms a traditional method of estimating RSD without utilizing manual annotation. Further, this paper provides a deeper insight into the deep learning network through visualization and interpretation of the features that are automatically learned.

Connected through hyperedge HE-1 (induced by source paper "future state predicting lstm for early surgery type recognition"):
  with members:
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "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-2 (induced by source paper "unsupervised temporal video segmentation as an auxiliary task for predicting the remaining surgery duration"):
  with members:
    - "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"
    - "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-3 (induced by source paper "learning from a tiny dataset of manual annotations a teacher student approach for surgical phase recognition"):
  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"
    - "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-4 (induced by source paper "prediction of laparoscopic procedure duration using unlabeled multimodal sensor data"):
  with members:
    - "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
    - "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-5 (induced by source paper "am i done predicting action progress in videos"):
  with members:
    - "action completion a temporal model for moment detection"
    - "guess where actor supervision for spatiotemporal action localization"
    - "temporal recurrent networks for online action detection"
    - "what actions are needed for understanding human actions in videos"
    - "temporal action detection with structured segment networks"
    - "action tubelet detector for spatio temporal action localization"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "temporal action localization by structured maximal sums"

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)