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 "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"):
  Abstract: Most current semantic segmentation approaches fall back on deep convolutional neural networks (CNNs). However, their use of convolution operations with local receptive fields causes failures in modeling contextual spatial relations. Prior works have sought to address this issue by using graphical models or spatial propagation modules in networks. But such models often fail to capture long-range spatial relationships between entities, which leads to spatially fragmented predictions. Moreover, recent works have demonstrated that channel-wise information also acts a pivotal part in CNNs. In this work, we introduce two simple yet effective network units, the spatial relation module and the channel relation module, to learn and reason about global relationships between any two spatial positions or feature maps, and then produce relation-augmented feature representations. The spatial and channel relation modules are general and extensible, and can be used in a plug-and-play fashion with the existing fully convolutional network (FCN) framework. We evaluate relation module-equipped networks on semantic segmentation tasks using two aerial image datasets, which fundamentally depend on long-range spatial relational reasoning. The networks achieve very competitive results, bringing significant improvements over baselines.

Includes members:
  Member "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf")
  Member "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")
  Member "depth estimation via affinity learned with convolutional spatial propagation network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "adaptive affinity fields for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "high order paired aspp networks for semantic segmenation")
  Member "switchable temporal propagation network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning correspondence from the cycle consistency of time")
  Member "relation networks for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "proposal learning for semi supervised object detection")
  Member "a simple neural network module for relational reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "learning affinity via spatial propagation networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "arma nets expanding receptive field for dense prediction")

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)