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: "tree structured kronecker convolutional network for semantic segmentation"
  Abstract: Most existing semantic segmentation methods employ atrous convolution to enlarge the receptive field of filters, but neglect partial information. To tackle this issue, we firstly propose a novel Kronecker convolution which adopts Kronecker product to expand the standard convolutional kernel for taking into account the partial feature neglected by atrous convolutions. Therefore, it can capture partial information and enlarge the receptive field of filters simultaneously without introducing extra parameters. Secondly, we propose Tree-structured Feature Aggregation (TFA) module which follows a recursive rule to expand and forms a hierarchical structure. Thus, it can naturally learn representations of multi-scale objects and encode hierarchical contextual information in complex scenes. Finally, we design Tree-structured Kronecker Convolutional Networks (TKCN) which employs Kronecker convolution and TFA module. Extensive experiments on three datasets, PASCAL VOC 2012, PASCAL-Context and Cityscapes, verify the effectiveness of our proposed approach. We make the code and the trained model publicly available at this https URL.

Connected through hyperedge HE-1 (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"
    - "searching for efficient multi scale architectures for dense image prediction"

Connected through hyperedge HE-2 (induced by source paper "efficient ladder style densenets for semantic segmentation of large images"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "on the convergence of adam and beyond"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "gated feedback refinement network for coarse to fine dense semantic image labeling"
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "interlaced sparse self attention for semantic segmentation"):
  with members:
    - "deeperlab single shot image parser"
    - "generating long sequences with sparse transformers"
    - "semantic correlation promoted shape variant context for segmentation"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "devil in the details towards accurate single and multiple human parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "consensus feature network for scene parsing"):
  with members:
    - "asymmetric non local neural networks for semantic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "adaptive context network for scene parsing"
    - "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"
    - "ccnet criss cross attention for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "cgnet a light weight context guided network for semantic segmentation"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "context encoding for semantic segmentation"

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)