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: "semantic segmentation with boundary neural fields"
  Abstract: The state-of-the-art in semantic segmentation is currently represented by fully convolutional networks (FCNs). However, FCNs use large receptive fields and many pooling layers, both of which cause blurring and low spatial resolution in the deep layers. As a result FCNs tend to produce segmentations that are poorly localized around object boundaries. Prior work has attempted to address this issue in post-processing steps, for example using a color-based CRF on top of the FCN predictions. However, these approaches require additional parameters and low-level features that are difficult to tune and integrate into the original network architecture. Additionally, most CRFs use color-based pixel affinities, which are not well suited for semantic segmentation and lead to spatially disjoint predictions. #R##N#To overcome these problems, we introduce a Boundary Neural Field (BNF), which is a global energy model integrating FCN predictions with boundary cues. The boundary information is used to enhance semantic segment coherence and to improve object localization. Specifically, we first show that the convolutional filters of semantic FCNs provide good features for boundary detection. We then employ the predicted boundaries to define pairwise potentials in our energy. Finally, we show that our energy decomposes semantic segmentation into multiple binary problems, which can be relaxed for efficient global optimization. We report extensive experiments demonstrating that minimization of our global boundary-based energy yields results superior to prior globalization methods, both quantitatively as well as qualitatively.

Connected through hyperedge HE-1 (induced by source paper "cafenet class agnostic few shot edge detection network"):
  with members:
    - "transductive few shot learning with meta learned confidence"
    - "dense classification and implanting for few shot learning"
    - "bi directional cascade network for perceptual edge detection"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "dynamic feature fusion for semantic edge detection"
    - "panet few shot image semantic segmentation with prototype alignment"

Connected through hyperedge HE-2 (induced by source paper "gated scnn gated shape cnns for semantic segmentation"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "adaptive affinity fields for semantic segmentation"
    - "free form image inpainting with gated convolution"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "tree structured kronecker convolutional network for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "adversarial structure matching for structured prediction tasks"):
  with members:
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "taskonomy disentangling task transfer learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "regularizing deep networks by modeling and predicting label structure"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "interpreting deep visual representations via network dissection"
    - "learning affinity via spatial propagation networks"

Connected through hyperedge HE-4 (induced by source paper "learning structure via consensus for face segmentation and parsing"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "does generative face completion help face recognition"
    - "face mask extraction in video sequence"
    - "learning discriminators as energy networks in adversarial learning"
    - "adversarial structure matching loss for image segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "face completion with semantic knowledge and collaborative adversarial learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "classifying segmenting and tracking object instances in video with mask propagation"):
  with members:
    - "large scale object mining for object discovery from unlabeled video"
    - "video instance segmentation"
    - "learning temporal pose estimation from sparsely labeled videos"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "object detection in video with spatiotemporal sampling networks"
    - "efficient video object segmentation via network modulation"
    - "exploring the limits of weakly supervised pretraining"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "resunet a a deep learning framework for semantic segmentation of remotely sensed data"):
  with members:
    - "rethinking imagenet pre training"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "road extraction by deep residual u net"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "context encoding for semantic segmentation"
    - "semantic labeling in very high resolution images via a self cascaded convolutional neural network"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "adaptive affinity fields for semantic segmentation"
    - "switchable temporal propagation network"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"
    - "learning affinity via spatial propagation networks"

Connected through hyperedge HE-8 (induced by source paper "distance transform regression for spatially aware deep semantic segmentation"):
  with members:
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "casenet deep category aware semantic edge detection"
    - "richer convolutional features for edge detection"
    - "multi task learning for segmentation of building footprints with deep neural networks"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"

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)