human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "semantic flow for fast and accurate scene parsing"):
  Abstract: In this paper, we focus on effective methods for fast and accurate scene parsing. A common practice to improve the performance is to attain high resolution feature maps with strong semantic representation. Two strategies are widely used---astrous convolutions and feature pyramid fusion, are either computation intensive or ineffective. Inspired by Optical Flow for motion alignment between adjacent video frames, we propose a Flow Alignment Module (FAM) to learn Semantic Flow between feature maps of adjacent levels and broadcast high-level features to high resolution features effectively and efficiently. Furthermore, integrating our module to a common feature pyramid structure exhibits superior performance over other real-time methods even on very light-weight backbone networks, such as ResNet-18. Extensive experiments are conducted on several challenging datasets, including Cityscapes, PASCAL Context, ADE20K and CamVid. Particularly, our network is the first to achieve 80.4\% mIoU on Cityscapes with a frame rate of 26 FPS. The code will be available at \url{this https URL}.

Includes members:
  Member "expectation maximization attention networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "super resolution using segmentation prior self attention generative adversarial network")
  Member "customizable architecture search for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast neural network adaptation via parameter remapping and architecture search")
  Member "dfanet deep feature aggregation for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "panoptic feature pyramid networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "ocnet object context network for scene parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "guided upsampling network for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "alignseg feature aligned segmentation networks")
  Member "espnetv2 a light weight power efficient and general purpose convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "low latency video semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dual temporal memory network for efficient video object segmentation")

Question: Please tell me which class the source paper 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)