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 "beyond rgb very high resolution urban remote sensing with multimodal deep networks"):
  Abstract: In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data. Especially, we study how deep fully convolutional networks can be adapted to deal with multi-modal and multi-scale remote sensing data for semantic labeling. Our contributions are threefold: a) we present an efficient multi-scale approach to leverage both a large spatial context and the high resolution data, b) we investigate early and late fusion of Lidar and multispectral data, c) we validate our methods on two public datasets with state-of-the-art results. Our results indicate that late fusion make it possible to recover errors steaming from ambiguous data, while early fusion allows for better joint-feature learning but at the cost of higher sensitivity to missing data.

Includes members:
  Member "joint learning from earth observation and openstreetmap data to get faster better semantic maps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "dense semantic labeling of subdecimeter resolution images with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")
  Member "adversarial examples for semantic segmentation and object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "towards better exploiting convolutional neural networks for remote sensing scene classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convolution neural network architecture learning for remote sensing scene classification")
  Member "full resolution residual networks for semantic segmentation in street scenes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "two stream convolutional neural network for accurate rgb d fingertip detection using depth and edge information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficiently utilizing complex valued polsar image data via a multi task deep learning framework")
  Member "fully convolutional networks for dense semantic labelling of high resolution aerial imagery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "refinenet multi path refinement networks for high resolution semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention guided chained context aggregation for semantic 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.NE(Neural and Evolutionary Computing)