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: "higher order conditional random fields in deep neural networks"
  Abstract: We address the problem of semantic segmentation using deep learning. Most segmentation systems include a Conditional Random Field (CRF) to produce a structured output that is consistent with the image's visual features. Recent deep learning approaches have incorporated CRFs into Convolutional Neural Networks (CNNs), with some even training the CRF end-to-end with the rest of the network. However, these approaches have not employed higher order potentials, which have previously been shown to significantly improve segmentation performance. In this paper, we demonstrate that two types of higher order potential, based on object detections and superpixels, can be included in a CRF embedded within a deep network. We design these higher order potentials to allow inference with the differentiable mean field algorithm. As a result, all the parameters of our richer CRF model can be learned end-to-end with our pixelwise CNN classifier. We achieve state-of-the-art segmentation performance on the PASCAL VOC benchmark with these trainable higher order potentials.

Connected through hyperedge HE-1 (induced by source paper "image embedded segmentation combining supervised and unsupervised objectives through generative adversarial networks"):
  with members:
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "mild net minimal information loss dilated network for gland instance segmentation in colon histology images"
    - "hybridnet classification and reconstruction cooperation for semi supervised learning"
    - "deep adversarial training for multi organ nuclei segmentation in histopathology images"
    - "gland instance segmentation using deep multichannel neural networks"
    - "semantic segmentation using adversarial networks"
    - "augmenting supervised neural networks with unsupervised objectives for large scale image classification"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "semantic correlation promoted shape variant context for segmentation"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "aanet attribute attention network for person re identifications"
    - "scene graph generation with external knowledge and image reconstruction"
    - "towards robust curve text detection with conditional spatial expansion"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "a network structure to explicitly reduce confusion errors in semantic segmentation"
    - "a classification refinement strategy for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "the apolloscape open dataset for autonomous driving and its application"):
  with members:
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deepmvs learning multi view stereopsis"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "boundary aware feature propagation for scene segmentation"):
  with members:
    - "unpaired image captioning via scene graph alignments"
    - "feature boosting network for 3d pose estimation"
    - "scene graph generation with external knowledge and image reconstruction"
    - "adaptive context network for scene parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "dual graph convolutional network for semantic segmentation"):
  with members:
    - "global aggregation then local distribution in fully convolutional networks"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "graph based global reasoning networks"

Connected through hyperedge HE-6 (induced by source paper "upsnet a unified panoptic segmentation network"):
  with members:
    - "panoptic segmentation"
    - "attention guided unified network for panoptic segmentation"
    - "learning to fuse things and stuff"
    - "unified perceptual parsing for scene understanding"
    - "panoptic segmentation with a joint semantic and instance segmentation network"
    - "path aggregation network for instance segmentation"
    - "weakly and semi supervised panoptic segmentation"
    - "horovod fast and easy distributed deep learning in tensorflow"

Connected through hyperedge HE-7 (induced by source paper "bipartite conditional random fields for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "seamless scene segmentation"
    - "deeperlab single shot image parser"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "panoptic feature pyramid networks"
    - "attention guided unified network for panoptic segmentation"

Connected through hyperedge HE-8 (induced by source paper "choicenet cnn learning through choice of multiple feature map representations"):
  with members:
    - "adaptive affinity fields for semantic segmentation"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "understanding convolution for semantic segmentation"
    - "residual conv deconv grid network for semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"

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)