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 "learning from a tiny dataset of manual annotations a teacher student approach for surgical phase recognition"):
  Abstract: Vision algorithms capable of interpreting scenes from a real-time video stream are necessary for computer-assisted surgery systems to achieve context-aware behavior. In laparoscopic procedures one particular algorithm needed for such systems is the identification of surgical phases, for which the current state of the art is a model based on a CNN-LSTM. A number of previous works using models of this kind have trained them in a fully supervised manner, requiring a fully annotated dataset. Instead, our work confronts the problem of learning surgical phase recognition in scenarios presenting scarce amounts of annotated data (under 25% of all available video recordings). We propose a teacher/student type of approach, where a strong predictor called the teacher, trained beforehand on a small dataset of ground truth-annotated videos, generates synthetic annotations for a larger dataset, which another model - the student - learns from. In our case, the teacher features a novel CNN-biLSTM-CRF architecture, designed for offline inference only. The student, on the other hand, is a CNN-LSTM capable of making real-time predictions. Results for various amounts of manually annotated videos demonstrate the superiority of the new CNN-biLSTM-CRF predictor as well as improved performance from the CNN-LSTM trained using synthetic labels generated for unannotated videos. For both offline and online surgical phase recognition with very few annotated recordings available, this new teacher/student strategy provides a valuable performance improvement by efficiently leveraging the unannotated data.

Includes members:
  Member "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "future state predicting lstm for early surgery type recognition")
  Member "temporal coherence based self supervised learning for laparoscopic workflow analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised temporal video segmentation as an auxiliary task for predicting the remaining surgery duration")
  Member "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction")
  Member "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction")
  Member "data distillation towards omni supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "few shot few shot learning and the role of spatial attention")
  Member "single and multi task architectures for tool presence detection challenge at m2cai 2016":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "laptool net a contextual detector of surgical tools in laparoscopic videos based on recurrent convolutional neural networks")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "endonet a deep architecture for recognition tasks on laparoscopic videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction")

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.LG(Machine Learning)