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 likelihood learning of a generic cnn crf model for semantic segmentation"
  Abstract: Deep Models, such as Convolutional Neural Networks (CNNs), are omnipresent in computer vision, as well as, structured models, such as Conditional Random Fields (CRFs). Combining them brings many advantages, foremost the ability to in-cooperate prior knowledge into CNNs, e.g. by explicitly modelling the dependencies between output variables. In this work we present a CRF model were unary factors are dependent on a CNN. Our main contribution is an efficient and scalable, maximum likelihood-based, learning procedure to infer all model parameters jointly. Previous work either concentrated on piecewise-training, or maximum likelihood learning of restricted model families, such as Gaussian CRFs or CRFs with a few variables only. In contrast, we are the first to perform maximum likelihood learning for large-sized factor graphs with non-parametric potentials. We have applied our model to the task of semantic labeling of body parts in depth images. We show that it is superior to selected competing models and learning strategies. Furthermore, we empirically observe that our model can capture shape and context information of relating body parts.

Connected through hyperedge HE-1 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems"):
  with members:
    - "hierarchical deep stereo matching on high resolution images"
    - "ga net guided aggregation net for end to end stereo matching"
    - "learned collaborative stereo refinement"
    - "learning depth with convolutional spatial propagation network"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"

Connected through hyperedge HE-2 (induced by source paper "deepigeos a deep interactive geodesic framework for medical image segmentation"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "a survey on deep learning in medical image analysis"
    - "end to end tracking and semantic segmentation using recurrent neural networks"
    - "instance sensitive fully convolutional networks"
    - "temporal convolutional networks for action segmentation and detection"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "pixel level encoding and depth layering for instance level semantic labeling"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "what makes for effective detection proposals"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "instance aware semantic segmentation via multi task network cascades"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "end to end training of hybrid cnn crf models for stereo"):
  with members:
    - "sublabel accurate convex relaxation of vectorial multilabel energies"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "complexity of discrete energy minimization problems"
    - "solving dense image matching in real time using discrete continuous optimization"
    - "semantic image segmentation via deep parsing network"
    - "computing the stereo matching cost with a convolutional neural network"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "local perturb and map for structured prediction"):
  with members:
    - "high for low and low for high efficient boundary detection from deep object features and its applications to high level vision"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"
    - "learning graphical model parameters with approximate marginal inference"
    - "on the partition function and random maximum a posteriori perturbations"

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)