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: "square cut a segmentation algorithm on the basis of a rectangle shape"
  Abstract: We present a rectangle-based segmentation algorithm that sets up a graph and performs a graph cut to separate an object from the background. However, graph-based algorithms distribute the graph's nodes uniformly and equidistantly on the image. Then, a smoothness term is added to force the cut to prefer a particular shape. This strategy does not allow the cut to prefer a certain structure, especially when areas of the object are indistinguishable from the background. We solve this problem by referring to a rectangle shape of the object when sampling the graph nodes, i.e., the nodes are distributed non-uniformly and non-equidistantly on the image. This strategy can be useful, when areas of the object are indistinguishable from the background. For evaluation, we focus on vertebrae images from Magnetic Resonance Imaging (MRI) datasets to support the time consuming manual slice-by-slice segmentation performed by physicians. The ground truth of the vertebrae boundaries were manually extracted by two clinical experts (neurological surgeons) with several years of experience in spine surgery and afterwards compared with the automatic segmentation results of the proposed scheme yielding an average Dice Similarity Coefficient (DSC) of 90.97±2.2%.

Connected through hyperedge HE-1 (induced by source paper "in depth assessment of an interactive graph based approach for the segmentation for pancreatic metastasis in ultrasound acquisitions of the liver with two specialists in internal medicine"):
  with members:
    - "htc vive mevislab integration via openvr for medical applications"
    - "interactive outlining of pancreatic cancer liver metastases in ultrasound images"
    - "us cut interactive algorithm for rapid detection and segmentation of liver tumors in ultrasound acquisitions"
    - "interactive volumetry of liver ablation zones"
    - "refinement cut user guided segmentation algorithm for translational science"
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "gbm volumetry using the 3d slicer medical image computing platform"
    - "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab"

Connected through hyperedge HE-2 (induced by source paper "interactive outlining of pancreatic cancer liver metastases in ultrasound images"):
  with members:
    - "us cut interactive algorithm for rapid detection and segmentation of liver tumors in ultrasound acquisitions"
    - "interactive volumetry of liver ablation zones"
    - "refinement cut user guided segmentation algorithm for translational science"
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "gbm volumetry using the 3d slicer medical image computing platform"
    - "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab"
    - "pcg cut graph driven segmentation of the prostate central gland"
    - "template cut a pattern based segmentation paradigm"

Connected through hyperedge HE-3 (induced by source paper "algorithm guided outlining of 105 pancreatic cancer liver metastases in ultrasound"):
  with members:
    - "htc vive mevislab integration via openvr for medical applications"
    - "interactive outlining of pancreatic cancer liver metastases in ultrasound images"
    - "interactive volumetry of liver ablation zones"
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab"
    - "pcg cut graph driven segmentation of the prostate central gland"
    - "template cut a pattern based segmentation paradigm"

Connected through hyperedge HE-4 (induced by source paper "htc vive mevislab integration via openvr for medical applications"):
  with members:
    - "interactive volumetry of liver ablation zones"
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "gbm volumetry using the 3d slicer medical image computing platform"
    - "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab"
    - "pcg cut graph driven segmentation of the prostate central gland"
    - "pituitary adenoma volumetry with 3d slicer"
    - "detection and visualization of endoleaks in ct data for monitoring of thoracic and abdominal aortic aneurysm stents"

Connected through hyperedge HE-5 (induced by source paper "vertebral body segmentation with growcut initial experience workflow and practical application"):
  with members:
    - "refinement cut user guided segmentation algorithm for translational science"
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "gbm volumetry using the 3d slicer medical image computing platform"
    - "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab"
    - "pituitary adenoma volumetry with 3d slicer"

Connected through hyperedge HE-6 (induced by source paper "interactive and scale invariant segmentation of the rectum sigmoid via user defined templates"):
  with members:
    - "interactive volumetry of liver ablation zones"
    - "refinement cut user guided segmentation algorithm for translational science"
    - "development of an open source software module for enhanced visualization during mr guided interstitial gynecologic brachytherapy"
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "gbm volumetry using the 3d slicer medical image computing platform"
    - "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab"
    - "pcg cut graph driven segmentation of the prostate central gland"
    - "image guided therapy system for interstitial gynecologic brachytherapy in a multimodality operating suite"

Connected through hyperedge HE-7 (induced by source paper "cellular automata segmentation of the boundary between the compacta of vertebral bodies and surrounding structures"):
  with members:
    - "interactive volumetry of liver ablation zones"
    - "refinement cut user guided segmentation algorithm for translational science"
    - "development of an open source software module for enhanced visualization during mr guided interstitial gynecologic brachytherapy"
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "gbm volumetry using the 3d slicer medical image computing platform"
    - "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab"
    - "image guided therapy system for interstitial gynecologic brachytherapy in a multimodality operating suite"
    - "template cut a pattern based segmentation paradigm"

Connected through hyperedge HE-8 (induced by source paper "us cut interactive algorithm for rapid detection and segmentation of liver tumors in ultrasound acquisitions"):
  with members:
    - "interactive volumetry of liver ablation zones"
    - "refinement cut user guided segmentation algorithm for translational science"
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab"
    - "template cut a pattern based segmentation paradigm"
    - "aorta segmentation for stent simulation"
    - "a flexible semi automatic approach for glioblastoma multiforme segmentation"
    - "pituitary adenoma segmentation"

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)