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: "learning a discriminative feature network for semantic segmentation"
  Abstract: Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction. To tackle these two problems, we propose a Discriminative Feature Network (DFN), which contains two sub-networks: Smooth Network and Border Network. Specifically, to handle the intra-class inconsistency problem, we specially design a Smooth Network with Channel Attention Block and global average pooling to select the more discriminative features. Furthermore, we propose a Border Network to make the bilateral features of boundary distinguishable with deep semantic boundary supervision. Based on our proposed DFN, we achieve state-of-the-art performance 86.2% mean IOU on PASCAL VOC 2012 and 80.3% mean IOU on Cityscapes dataset.

Connected through hyperedge HE-1 (induced by source paper "attention guided chained context aggregation for semantic segmentation"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "selective kernel networks"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "ocnet object context network for scene parsing"
    - "ccnet criss cross attention for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks"):
  with members:
    - "acfnet attentional class feature network for semantic segmentation"
    - "asymmetric non local neural networks for semantic segmentation"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "efficient segmentation learning downsampling near semantic boundaries"
    - "boundary aware feature propagation for scene segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (induced by source paper "high order paired aspp networks for semantic segmenation"):
  with members:
    - "asymmetric non local neural networks for semantic segmentation"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "mixed high order attention network for person re identification"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"
    - "unified perceptual parsing for scene understanding"

Connected through hyperedge HE-4 (induced by source paper "alignseg feature aligned segmentation networks"):
  with members:
    - "gated scnn gated shape cnns for semantic segmentation"
    - "indices matter learning to index for deep image matting"
    - "adaptive context network for scene parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "guided upsampling network for real time semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"
    - "unified perceptual parsing for scene understanding"

Connected through hyperedge HE-5 (induced by source paper "learning delicate local representations for multi person pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "res2net a new multi scale backbone architecture"
    - "multi person pose estimation with enhanced channel wise and spatial information"
    - "omni scale feature learning for person re identification"
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "multi scale structure aware network for human pose estimation"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-6 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "acfnet attentional class feature network for semantic segmentation"
    - "asymmetric non local neural networks for semantic segmentation"
    - "object contextual representations for semantic segmentation"
    - "eca net efficient channel attention for deep convolutional neural networks"
    - "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "bisenet bilateral segmentation network for real time semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "night time semantic segmentation with a large real dataset"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "expectation maximization attention networks for semantic segmentation"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "spgnet semantic prediction guidance for scene parsing"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "structured knowledge distillation for dense prediction"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ocnet object context network for scene parsing"

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)