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: "cfcm segmentation via coarse to fine context memory"
  Abstract: Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales. Although yielding good results, even deeper architectures and alternative methods for feature fusion at different resolutions have been scarcely investigated for medical applications. In this work we propose to implement segmentation via an encoder-decoder architecture which differs from any other previously published method since (i) it employs a very deep architecture based on residual learning and (ii) combines features via a convolutional Long Short Term Memory (LSTM), instead of concatenation or summation. The intuition is that the memory mechanism implemented by LSTMs can better integrate features from different scales through a coarse-to-fine strategy; hence the name Coarse-to-Fine Context Memory (CFCM). We demonstrate the remarkable advantages of this approach on two datasets: the Montgomery county lung segmentation dataset, and the EndoVis 2015 challenge dataset for surgical instrument segmentation.

Connected through hyperedge HE-1 (induced by source paper "2017 robotic instrument segmentation challenge"):
  with members:
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "simultaneous recognition and pose estimation of instruments in minimally invasive surgery"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "deep residual learning for instrument segmentation in robotic surgery"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"
    - "deeper depth prediction with fully convolutional residual networks"
    - "holistically nested edge detection"

Connected through hyperedge HE-2 (induced by source paper "incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video"):
  with members:
    - "u netplus a modified encoder decoder u net architecture for semantic and instance segmentation of surgical instrument"
    - "2017 robotic instrument segmentation challenge"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "attention u net learning where to look for the pancreas"
    - "multiview two task recursive attention model for left atrium and atrial scars segmentation"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"

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)