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 "temporal segmentation of surgical sub tasks through deep learning with multiple data sources"):
  Abstract: Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an observation (such as bleeding). A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs. The objective of this work is to estimate the current state of the surgical task based on the actions performed or events occurred as the task progresses. We propose Fusion-KVE, a unified surgical state estimation model that incorporates multiple data sources including the Kinematics, Vision, and system Events. Additionally, we examine the strengths and weaknesses of different state estimation models in segmenting states with different representative features or levels of granularity. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), as well as a more complex dataset involving robotic intra-operative ultrasound (RIOUS) imaging, created using the da Vinci Xi surgical system. Our model achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgical state estimation models in both JIGSAWS suturing dataset and our RIOUS dataset.

Includes members:
  Member "weakly supervised recognition of surgical gestures":
    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 "learning from a tiny dataset of manual annotations a teacher student approach for surgical phase recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automated surgical activity recognition with one labeled sequence")
  Member "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding":
    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 "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-4 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction")
  Member "surgical activity recognition in robot assisted radical prostatectomy using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "novel evaluation of surgical activity recognition models using task based efficiency metrics")
  Member "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation")
  Member "recognizing surgical activities with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction")
  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.CV(Computer Vision and Pattern Recognition)