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: "joint training of generic cnn crf models with stochastic optimization"
  Abstract: We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Random Field (CRF) parameters. While stochastic gradient descent is a standard technique for CNN training, it was not used for joint models so far. We show that our learning method is (i) general, i.e. it applies to arbitrary CNN and CRF architectures and potential functions; (ii) scalable, i.e. it has a low memory footprint and straightforwardly parallelizes on GPUs; (iii) easy in implementation. Additionally, the unified CNN-CRF optimization approach simplifies a potential hardware implementation. We empirically evaluate our method on the task of semantic labeling of body parts in depth images and show that it compares favorably to competing techniques.

Connected through hyperedge HE-1 (induced by source paper "a projected gradient descent method for crf inference allowing end to end training of arbitrary pairwise potentials"):
  with members:
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "laplacian reconstruction and refinement for semantic segmentation"
    - "efficient continuous relaxations for dense crf"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "semantic image segmentation via deep parsing network"

Connected through hyperedge HE-2 (induced by source paper "toward robustness against label noise in training deep discriminative neural networks"):
  with members:
    - "learning from noisy large scale datasets with minimal supervision"
    - "webly supervised learning of convolutional networks"
    - "seeing through the human reporting bias visual classifiers from noisy human centric labels"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "deep residual learning for image recognition"
    - "fully connected deep structured networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "deep occlusion reasoning for multi camera multi target detection"):
  with members:
    - "the wildtrack multi camera person dataset"
    - "deep multi camera people detection"
    - "how far are we from solving pedestrian detection"
    - "ssd single shot multibox detector"
    - "multi modal mean fields via cardinality based clamping"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "principled parallel mean field inference for discrete random fields"
    - "higher order potentials in end to end trainable conditional random fields"

Connected through hyperedge HE-4 (induced by source paper "conditional random field and deep feature learning for hyperspectral image segmentation"):
  with members:
    - "learning aerial image segmentation from online maps"
    - "structured feature learning for pose estimation"
    - "crf cnn modeling structured information in human pose estimation"
    - "crf learning with cnn features for image segmentation"
    - "closed form training of conditional random fields for large scale image segmentation"
    - "convolutional neural networks at constrained time cost"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "fast semantic image segmentation with high order context and guided filtering"):
  with members:
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "deeply learning the messages in message passing inference"
    - "deep image scaling up image recognition"
    - "fast guided filter"
    - "conditional random fields as recurrent neural networks"
    - "semantic image segmentation with task specific edge detection using cnns and a discriminatively trained domain transform"
    - "deep residual learning for image recognition"

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)