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 edge detection with diverse deep supervision"
  Abstract: Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition. SED naturally requires achieving two distinct supervision targets: locating fine detailed edges and identifying high-level semantics. We shed light on how such distracted supervision targets prevent state-of-the-art SED methods from effectively using deep supervision to improve results. In this paper, we propose a novel fully convolutional neural network architecture using diverse deep supervision (DDS) within a multi-task framework where lower layers aim at generating category-agnostic edges, while higher layers are responsible for the detection of category-aware semantic edges. To overcome the distracted supervision challenge, a novel information converter unit is introduced, whose effectiveness has been extensively evaluated in several popular benchmark datasets, including SBD, Cityscapes, and PASCAL VOC2012. Source code will be released upon paper acceptance.

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 "elkppnet an edge aware neural network with large kernel pyramid pooling for learning discriminative features in semantic segmentation"):
  with members:
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "focal loss for dense object detection"
    - "incorporating depth into both cnn and crf for indoor semantic segmentation"
    - "richer convolutional features for edge detection"
    - "on support relations and semantic scene graphs"

Connected through hyperedge HE-3 (induced by source paper "se2net siamese edge enhancement network for salient object detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "deep attentive tracking via reciprocative learning"
    - "salient object detection via high to low hierarchical context aggregation"
    - "reverse attention for salient object detection"
    - "casenet deep category aware semantic edge 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-4 (induced by source paper "panoptic edge detection"):
  with members:
    - "panoptic segmentation"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "dynamic feature fusion for semantic edge detection"
    - "panoptic feature pyramid networks"
    - "simultaneous edge alignment and learning"
    - "learning to predict crisp boundaries"
    - "richer convolutional features for edge detection"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "dynamic feature fusion for semantic edge detection"):
  with members:
    - "simultaneous edge alignment and learning"
    - "casenet deep category aware semantic edge detection"
    - "richer convolutional features for edge detection"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "ssd single shot multibox detector"
    - "feature pyramid networks for object detection"
    - "high for low and low for high efficient boundary detection from deep object features and its applications to high level vision"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "doobnet deep object occlusion boundary detection from an image"):
  with members:
    - "focal loss for dense object detection"
    - "richer convolutional features for edge detection"
    - "dilated residual networks"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "doc deep occlusion estimation from a single image"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "pixel wise deep learning for contour detection"

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)