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: "a flexible semi automatic approach for glioblastoma multiforme segmentation"
  Abstract: Gliomas are the most common primary brain tumors, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the preoperative tumor volume is essential. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming process that can be overcome with the help of segmentation methods. In this paper, a flexible semi-automatic approach for grade IV glioma segmentation is presented. The approach uses a novel segmentation scheme for spherical objects that creates a directed 3D graph. Thereafter, the minimal cost closed set on the graph is computed via a polynomial time s-t cut, creating an optimal segmentation of the tumor. The user can improve the results by specifying an arbitrary number of additional seed points to support the algorithm with grey value information and geometrical constraints. The presented method is tested on 12 magnetic resonance imaging datasets. The ground truth of the tumor boundaries are manually extracted by neurosurgeons. The segmented gliomas are compared with a one click method, and the semi-automatic approach yields an average Dice Similarity Coefficient (DSC) of 77.72% and 83.91%, respectively.

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 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-3 (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-4 (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"
    - "square cut a segmentation algorithm on the basis of a rectangle shape"
    - "aorta segmentation for stent simulation"
    - "pituitary adenoma segmentation"

Connected through hyperedge HE-5 (induced by source paper "refinement cut user guided segmentation algorithm for translational science"):
  with members:
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "gbm volumetry using the 3d slicer medical image computing platform"
    - "pcg cut graph driven segmentation of the prostate central gland"
    - "image guided therapy system for interstitial gynecologic brachytherapy in a multimodality operating suite"
    - "template cut a pattern based segmentation paradigm"
    - "pituitary adenoma volumetry with 3d slicer"
    - "square cut a segmentation algorithm on the basis of a rectangle shape"
    - "detection and visualization of endoleaks in ct data for monitoring of thoracic and abdominal aortic aneurysm stents"

Connected through hyperedge HE-6 (induced by source paper "gbm volumetry using the 3d slicer medical image computing platform"):
  with members:
    - "pituitary adenoma volumetry with 3d slicer"
    - "square cut a segmentation algorithm on the basis of a rectangle shape"
    - "evaluation of a novel approach for automatic volume determination of glioblastomas based on several manual expert segmentations"
    - "glioblastoma multiforme segmentation in mri data with a balloon inflation approach"

Connected through hyperedge HE-7 (induced by source paper "pcg cut graph driven segmentation of the prostate central gland"):
  with members:
    - "atlas based prostate segmentation using an hybrid registration"

Connected through hyperedge HE-8 (induced by source paper "square cut a segmentation algorithm on the basis of a rectangle shape"):
  with members:
    - "aorta segmentation for stent simulation"
    - "a comparison of two human brain tumor segmentation methods for mri data"

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.CE(Computational Engineering, Finance, and Science)