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: "unbiased shape compactness for segmentation"
  Abstract: We propose to constrain segmentation functionals with a dimensionless, unbiased and position-independent shape compactness prior, which we solve efficiently with an alternating direction method of multipliers (ADMM). Involving a squared sum of pairwise potentials, our prior results in a challenging high-order optimization problem, which involves dense (fully connected) graphs. We split the problem into a sequence of easier sub-problems, each performed efficiently at each iteration: (i) a sparse-matrix inversion based on Woodbury identity, (ii) a closed-form solution of a cubic equation and (iii) a graph-cut update of a sub-modular pairwise sub-problem with a sparse graph. We deploy our prior in an energy minimization, in conjunction with a supervised classifier term based on CNNs and standard regularization constraints. We demonstrate the usefulness of our energy in several medical applications. In particular, we report comprehensive evaluations of our fully automated algorithm over 40 subjects, showing a competitive performance for the challenging task of abdominal aorta segmentation in MRI.

Connected through hyperedge HE-1 (induced by source paper "discretely constrained deep network for weakly supervised segmentation"):
  with members:
    - "prior aware neural network for partially supervised multi organ segmentation"
    - "curriculum semi supervised segmentation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "beyond gradient descent for regularized segmentation losses"
    - "predicting deeper into the future of semantic segmentation"
    - "discrete continuous admm for transductive inference in higher order mrfs"
    - "dope distributed optimization for pairwise energies"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "multiregion segmentation of bladder cancer structures in mri with progressive dilated convolutional networks"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "gridnet with automatic shape prior registration for automatic mri cardiac segmentation"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "deep cnn ensembles and suggestive annotations for infant brain mri segmentation"

Connected through hyperedge HE-3 (induced by source paper "beyond gradient descent for regularized segmentation losses"):
  with members:
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "efficient graph cut optimization for full crfs with quantized edges"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "discrete continuous admm for transductive inference in higher order mrfs"
    - "dope distributed optimization for pairwise energies"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "training neural networks without gradients a scalable admm approach"

Connected through hyperedge HE-4 (induced by source paper "an ilp solver for multi label mrfs with connectivity constraints"):
  with members:
    - "superpixels an evaluation of the state of the art"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "the minimum cost connected subgraph problem in medical image analysis"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"

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)