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: "understanding soft tissue behavior for application to microlaparoscopic surface scan"
  Abstract: This paper presents an approach for understanding the soft-tissue behavior in surface contact with a probe scanning the tissue. The application domain is confocal microlaparoscopy, mostly used for imaging the outer surface of the organs in the abdominal cavity. The probe is swept over the tissue to collect sequential images to obtain a large field of view with mosaicking. The problem we address is that the tissue also moves with the probe due to its softness; therefore, the resulting mosaic is not in the same shape and dimension as traversed by the probe. Our approach is inspired by the finger slip studies and adapts the idea of load-slip phenomenon that explains the movement of the soft part of the finger when dragged on a hard surface. We propose the concept of loading-distance and perform measurements on beef liver and chicken breast tissues. We propose a protocol to determine the loading-distance prior to an automated scan and introduce an approach to compensate the tissue movement in raster scans. Our implementation and experiments show that we can have an image mosaic of the tissue surface in a desired rectangular shape with this approach.

Connected through hyperedge HE-1 (induced by source paper "intraoperative robotic assisted large area high speed microscopic imaging and intervention"):
  with members:
    - "autonomous scanning for endomicroscopic mosaicing and 3d fusion"

Connected through hyperedge HE-2 (induced by source paper "image computing for fibre bundle endomicroscopy a review"):
  with members:
    - "weakly supervised learning based feature localization in confocal laser endomicroscopy glioma images"
    - "effective deep learning training for single image super resolution in endomicroscopy exploiting video registration based reconstruction"
    - "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "improving utility of brain tumor confocal laser endomicroscopy objective value assessment and diagnostic frame detection with convolutional neural networks"
    - "automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"

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.RO(Robotics)