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 "cross connected networks for multi task learning of detection and segmentation"):
  Abstract: Multi-task learning improves generalization performance by sharing knowledge among related tasks. Existing models are for task combinations annotated on the same dataset, while there are cases where multiple datasets are available for each task. How to utilize knowledge of successful single-task CNNs that are trained on each dataset has been explored less than multi-task learning with a single dataset. We propose a cross-connected CNN, a new architecture that connects single-task CNNs through convolutional layers, which transfer useful information for the counterpart. We evaluated our proposed architecture on a combination of detection and segmentation using two datasets. Experiments on pedestrians show our CNN achieved a higher detection performance compared to baseline CNNs, while maintaining high quality for segmentation. It is the first known attempt to tackle multi-task learning with different training datasets between detection and segmentation. Experiments with wild birds demonstrate how our CNN learns general representations from limited datasets.

Includes members:
  Member "multi task learning using uncertainty to weigh losses for scene geometry and semantics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatial sampling network for fast scene understanding")
  Member "blitznet a real time deep network for scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "learning to segment every thing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "multi task self supervised visual learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "segflow joint learning for video object segmentation and optical flow":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "multi task learning for segmentation of building footprints with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf")
  Member "an overview of multi task learning in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "the cityscapes dataset for semantic urban scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)