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 "semantic segmentation from remote sensor data and the exploitation of latent learning for classification of auxiliary tasks"):
  Abstract: In this paper we address three different aspects of semantic segmentation from remote sensor data using deep neural networks. Firstly, we focus on the semantic segmentation of buildings from remote sensor data and propose ICT-Net. The proposed network has been tested on the INRIA and AIRS benchmark datasets and is shown to outperform all other state of the art by more than 1.5% and 1.8% on the Jaccard index, respectively. #R##N#Secondly, as the building classification is typically the first step of the reconstruction process, we investigate the relationship of the classification accuracy to the reconstruction accuracy. #R##N#Finally, we present the simple yet compelling concept of latent learning and the implications it carries within the context of deep learning. We posit that a network trained on a primary task (i.e. building classification) is unintentionally learning about auxiliary tasks (e.g. the classification of road, tree, etc) which are complementary to the primary task. We extensively tested the proposed technique on the ISPRS benchmark dataset which contains multi-label ground truth, and report an average classification accuracy (F1 score) of 54.29% (SD=17.03) for roads, 10.15% (SD=2.54) for cars, 24.11% (SD=5.25) for trees, 42.74% (SD=6.62) for low vegetation, and 18.30% (SD=16.08) for clutter. #R##N#The source code and supplemental material is publicly available at this http URL.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "self supervised generalisation with meta auxiliary learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to generalize one sample at a time with self supervision")
  Member "temporary removal aerial imagery for roof segmentation a large scale dataset towards automatic mapping of buildings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "progressively growing generative adversarial networks for high resolution semantic segmentation of satellite images")
  Member "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised domain adaptation via structurally regularized deep clustering")
  Member "automatic pixelwise object labeling for aerial imagery using stacked u nets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "progressively growing generative adversarial networks for high resolution semantic segmentation of satellite images")
  Member "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks")
  Member "auxiliary tasks in multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to generalize one sample at a time with self supervision")
  Member "ternausnetv2 fully convolutional network for instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep 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)