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: "loosecut interactive image segmentation with loosely bounded boxes"
  Abstract: One popular approach to interactively segment the foreground object of interest from an image is to annotate a bounding box that covers the foreground object. Then, a binary labeling is performed to achieve a refined segmentation. One major issue of the existing algorithms for such interactive image segmentation is their preference of an input bounding box that tightly encloses the foreground object. This increases the annotation burden, and prevents these algorithms from utilizing automatically detected bounding boxes. In this paper, we develop a new LooseCut algorithm that can handle cases where the input bounding box only loosely covers the foreground object. We propose a new Markov Random Fields (MRF) model for segmentation with loosely bounded boxes, including a global similarity constraint to better distinguish the foreground and background, and an additional energy term to encourage consistent labeling of similar-appearance pixels. This MRF model is then solved by an iterated max-flow algorithm. In the experiments, we evaluate LooseCut in three publicly-available image datasets, and compare its performance against several state-of-the-art interactive image segmentation algorithms. We also show that LooseCut can be used for enhancing the performance of unsupervised video segmentation and image saliency detection.

Connected through hyperedge HE-1 (induced by source paper "constrained dominant sets and its applications in computer vision"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "deep constrained dominant sets for person re identification"
    - "weakly supervised semantic segmentation using constrained dominant sets"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning neural models for end to end clustering"
    - "transductive label augmentation for improved deep network learning"
    - "multi feature fusion for image retrieval using constrained dominant sets"

Connected through hyperedge HE-2 (induced by source paper "iteratively trained interactive segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "annotating object instances with a polygon rnn"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "bridging category level and instance level semantic image segmentation"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "crowdcam dynamic region segmentation"):
  with members:
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "template matching with deformable diversity similarity"
    - "very deep convolutional networks for large scale image recognition"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-4 (induced by source paper "dominant sets for constrained image segmentation"):
  with members:
    - "multi target tracking in multiple non overlapping cameras using constrained dominant sets"
    - "interactive image segmentation using constrained dominant sets"

Connected through hyperedge HE-5 (induced by source paper "simple does it weakly supervised instance and semantic segmentation"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "learning to refine object segments"
    - "what makes for effective detection proposals"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "deep interactive object selection"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "interactive image segmentation using constrained dominant sets"):
  with members:
    - "neutro connectedness cut"

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)