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: "neutro connectedness cut"
  Abstract: Interactive image segmentation is a challenging task and receives increasing attention recently; however, two major drawbacks exist in interactive segmentation approaches. First, the segmentation performance of region of interest (ROI)-based methods is sensitive to the initial ROI: different ROIs may produce results with great difference. Second, most seed-based methods need intense interactions, and are not applicable in many cases. In this paper, we generalize the neutro-connectedness (NC) to be independent of top–down priors of objects and to model image topology with indeterminacy measurement on image regions, propose a novel method for determining object and background regions, which is applied to exclude isolated background regions and enforce label consistency, and put forward a hybrid interactive segmentation method, NC Cut (NC-Cut), which can overcome the above two problems by utilizing both pixelwise appearance information and region-based NC properties. We evaluate the proposed NC-Cut by employing two image data sets (265 images), and demonstrate that the proposed approach outperforms the state-of-the-art interactive image segmentation methods (Grabcut, MILCut, One-Cut, MGC   $_{\text {max}}^{\text {sum}}$   , and pPBC).

Connected through hyperedge HE-1 (induced by source paper "breast anatomy enriched tumor saliency estimation"):
  with members:
    - "tumor saliency estimation for breast ultrasound images via breast anatomy modeling"
    - "a benchmark for breast ultrasound image segmentation busis"
    - "a hybrid framework for tumor saliency estimation"
    - "automatic breast ultrasound image segmentation a survey"
    - "deep saliency with encoded low level distance map and high level features"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "tumor saliency estimation for breast ultrasound images via breast anatomy modeling"):
  with members:
    - "a benchmark for breast ultrasound image segmentation busis"
    - "automatic breast ultrasound image segmentation a survey"

Connected through hyperedge HE-3 (induced by source paper "swipecut interactive segmentation with diversified seed proposals"):
  with members:
    - "deep interactive object selection"

Connected through hyperedge HE-4 (induced by source paper "automatic breast ultrasound image segmentation a survey"):
  with members:
    - "fully convolutional networks for semantic segmentation"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "multi column deep neural networks for image classification"

Connected through hyperedge HE-5 (induced by source paper "interactive image segmentation using constrained dominant sets"):
  with members:
    - "loosecut interactive image segmentation with loosely bounded boxes"

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)