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: "efficient relaxations for dense crfs with sparse higher order potentials"
  Abstract: Dense conditional random fields (CRFs) with Gaussian pairwise potentials have become a popular framework for modelling several problems in computer vision such as stereo correspondence and multi-class semantic segmentation. By modelling long-range interactions, dense CRFs provide a more detailed labelling compared to their sparse counterparts. Currently the state-of-the-art algorithm performs mean-field inference using a filter-based method to obtain accurate segmentations, but fails to provide strong theoretical guarantees on the quality of the solution. Whilst the underlying model of a dense CRF provides enough information to yield well defined segmentations, it lacks the richness introduced via higher order potentials. The mean-field inference strategy was also extended to incorporate higher order potentials, but again failed to obtain a bound on the quality of the solution. To this extent, we show that a dense CRF can be aggregated with sparse higher order potentials in a way that is amenable to continuous relaxations. We will then show that, by using a filter-based method, these continuous relaxations can be optimised efficiently using state-of-the-art algorithms. Specifically we will solve a quadratic programming (QP) relaxation using the Frank-Wolfe algorithm and a linear programming (LP) relaxation by developing a proximal minimisation framework. By exploiting labelling consistency in the higher order potentials and utilising the filter-based method, we are able to formulate the above algorithms such that each iteration has a complexity linear in the number of classes and random variables. The experiments are performed on the standard publicly available MSRC data set and demonstrate the low energies achieved from the minimisation and the accuracy of the resulting segmentations.

Connected through hyperedge HE-1 (induced by source paper "gated crf loss for weakly supervised semantic image segmentation"):
  with members:
    - "rethinking imagenet pre training"
    - "towards closing the gap in weakly supervised semantic segmentation with dcnns combining local and global models"
    - "convolutional crfs for semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "i bet you are wrong gambling adversarial networks for structured semantic segmentation"):
  with members:
    - "understanding deep learning techniques for image segmentation"
    - "adversarial structure matching loss for image segmentation"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "student beats the teacher deep neural networks for lateral ventricles segmentation in brain mr"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "el gan embedding loss driven generative adversarial networks for lane detection"

Connected through hyperedge HE-3 (induced by source paper "reliability does matter an end to end weakly supervised semantic segmentation approach"):
  with members:
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "deeply supervised salient object detection with short connections"
    - "self produced guidance for weakly supervised object localization"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "adversarial complementary learning for weakly supervised object localization"
    - "encoder decoder with atrous separable convolution for semantic image 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)