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: "casenet deep category aware semantic edge detection"
  Abstract: Boundary and edge cues are highly beneficial in improving a wide variety of vision tasks such as semantic segmentation, object recognition, stereo, and object proposal generation. Recently, the problem of edge detection has been revisited and significant progress has been made with deep learning. While classical edge detection is a challenging binary problem in itself, the category-aware semantic edge detection by nature is an even more challenging multi-label problem. We model the problem such that each edge pixel can be associated with more than one class as they appear in contours or junctions belonging to two or more semantic classes. To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features. We then propose a multi-label loss function to supervise the fused activations. We show that our proposed architecture benefits this problem with better performance, and we outperform the current state-of-the-art semantic edge detection methods by a large margin on standard data sets such as SBD and Cityscapes.

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 "red net a recursive encoder decoder network for edge detection"):
  with members:
    - "richer convolutional features for edge detection"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "densely connected convolutional networks"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "deep hierarchical parsing for semantic segmentation"
    - "holistically nested edge detection"

Connected through hyperedge HE-4 (induced by source paper "distance transform regression for spatially aware deep semantic segmentation"):
  with members:
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "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"
    - "boundary aware instance segmentation"

Connected through hyperedge HE-5 (induced by source paper "se2net siamese edge enhancement network for salient object detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "semantic edge detection with diverse deep supervision"
    - "deep attentive tracking via reciprocative learning"
    - "salient object detection via high to low hierarchical context aggregation"
    - "reverse attention for salient object detection"
    - "richer convolutional features for edge detection"
    - "instance level salient object segmentation"
    - "amulet aggregating multi level convolutional features for salient object detection"

Connected through hyperedge HE-6 (induced by source paper "joint iris segmentation and localization using deep multi task learning framework"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "bam bottleneck attention module"
    - "learning a discriminative feature network for semantic segmentation"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-7 (induced by source paper "upi net semantic contour detection in placental ultrasound"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "spatial group wise enhance improving semantic feature learning in convolutional networks"
    - "bam bottleneck attention module"
    - "group normalization"
    - "attention guided unified network for panoptic segmentation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-8 (induced by source paper "sem lsd a learning based semantic line segment detector"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "ppgnet learning point pair graph for line segment detection"
    - "end to end wireframe parsing"
    - "cornernet detecting objects as paired keypoints"
    - "learning attraction field representation for robust line segment detection"
    - "comparison of semantic segmentation approaches for horizon sky line detection"
    - "focal loss for dense object detection"
    - "semantic visual localization"

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)