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 "attention guided chained context aggregation for semantic segmentation"):
  Abstract: Recent breakthroughs in semantic segmentation methods based on Fully Convolutional Networks (FCNs) have aroused great research interest. One of the critical issues is how to aggregate multi-scale contextual information effectively to obtain reliable results. To address this problem, we propose a novel paradigm called the Chained Context Aggregation Module (CAM). CAM gains features of various spatial scales through chain-connected ladder-style information flows. The features are then guided by Flow Guidance Connections to interact and fuse in a two-stage process, which we refer to as pre-fusion and re-fusion. We further adopt attention models in CAM to productively recombine and select those fused features to refine performance. Based on these developments, we construct the Chained Context Aggregation Network (CANet), which employs a two-step decoder to recover precise spatial details of prediction maps. We conduct extensive experiments on three challenging datasets, including Pascal VOC 2012, CamVid and SUN-RGBD. Results evidence that our CANet achieves state-of-the-art performance. Codes will be available on the publication of this paper.

Includes members:
  Member "gluoncv and gluonnlp deep learning in computer vision and natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leep a new measure to evaluate transferability of learned representations")
  Member "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient semantic video segmentation with per frame inference")
  Member "selective kernel networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "dfanet deep feature aggregation for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")
  Member "deep high resolution representation learning for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "bag of tricks for image classification with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rethinking image mixture for unsupervised visual representation learning")
  Member "ocnet object context network for scene parsing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "alignseg feature aligned segmentation networks")
  Member "learning a discriminative feature network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks")

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)