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 "multi task recurrent convolutional network with correlation loss for surgical video analysis"):
  Abstract: Surgical tool presence detection and surgical phase recognition are two fundamental yet challenging tasks in surgical video analysis and also very essential components in various applications in modern operating rooms. While these two analysis tasks are highly correlated in clinical practice as the surgical process is well-defined, most previous methods tackled them separately, without making full use of their relatedness. In this paper, we present a novel method by developing a multi-task recurrent convolutional network with correlation loss (MTRCNet-CL) to exploit their relatedness to simultaneously boost the performance of both tasks. Specifically, our proposed MTRCNet-CL model has an end-to-end architecture with two branches, which share earlier feature encoders to extract general visual features while holding respective higher layers targeting for specific tasks. Given that temporal information is crucial for phase recognition, long-short term memory (LSTM) is explored to model the sequential dependencies in the phase recognition branch. More importantly, a novel and effective correlation loss is designed to model the relatedness between tool presence and phase identification of each video frame, by minimizing the divergence of predictions from the two branches. Mutually leveraging both low-level feature sharing and high-level prediction correlating, our MTRCNet-CL method can encourage the interactions between the two tasks to a large extent, and hence can bring about benefits to each other. Extensive experiments on a large surgical video dataset (Cholec80) demonstrate outstanding performance of our proposed method, consistently exceeding the state-of-the-art methods by a large margin (e.g., 89.1% v.s. 81.0% for the mAP in tool presence detection and 87.4% v.s. 84.5% for F1 score in phase recognition). The code can be found on our project website.

Includes members:
  Member "incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video":
    also connected through other hyperedges:
      (none)
  Member "deep reinforcement learning for surgical gesture segmentation and classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction")
  Member "uncertainty in multitask learning joint representations for probabilistic mr only radiotherapy planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels")
  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 "deepphase surgical phase recognition in cataracts videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automatic gesture recognition in robot assisted surgery with reinforcement learning and tree search")
  Member "multi task learning of pairwise sequence classification tasks over disparate label spaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "beyond without forgetting multi task learning for classification with disjoint datasets")
  Member "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for automated medical image analysis")
  Member "fine grained recognition in the wild a multi task domain adaptation approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")

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)