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 "object contextual representations for semantic segmentation"):
  Abstract: In this paper, we address the semantic segmentation problem with a focus on the context aggregation strategy. Our motivation is that the label of a pixel is the category of the object that the pixel belongs to. We present a simple yet effective approach, object-contextual representations, characterizing a pixel by exploiting the representation of the corresponding object class. First, we learn object regions under the supervision of the ground-truth segmentation. Second, we compute the object region representation by aggregating the representations of the pixels lying in the object region. Last, % the representation similarity we compute the relation between each pixel and each object region, and augment the representation of each pixel with the object-contextual representation which is a weighted aggregation of all the object region representations according to their relations with the pixel. We empirically demonstrate that the proposed approach achieves competitive performance on various challenging semantic segmentation benchmarks: Cityscapes, ADE20K, LIP, PASCAL-Context, and COCO-Stuff.

Includes members:
  Member "learning compositional neural information fusion for human parsing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "acfnet attentional class feature network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks")
  Member "dual graph convolutional network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling")
  Member "expectation maximization attention networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic flow for fast and accurate scene parsing")
  Member "global aggregation then local distribution in fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "structured consistency loss for semi supervised semantic segmentation")
  Member "gated scnn gated shape cnns for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "searching for winograd aware quantized networks")
  Member "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "high resolution representations for labeling pixels and regions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "farsee net real time semantic segmentation by efficient multi scale context aggregation and feature space super resolution")

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)