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 "multi year vector dynamic time warping based crop mapping"):
  Abstract: Recent automated crop mapping via supervised learning-based methods have demonstrated unprecedented improvement over classical techniques. However, most crop mapping studies are limited to same-year crop mapping in which the present year's labeled data is used to predict the same year's crop map. Classification accuracies of these methods degrade considerably in cross-year mapping. Cross-year crop mapping is more useful as it allows the prediction of the following years' crop maps using previously labeled data. We propose Vector Dynamic Time Warping (VDTW), a novel multi-year classification approach based on warping of angular distances between phenological vectors. The results prove that the proposed VDTW method is robust to temporal and spectral variations compensating for different farming practices, climate and atmospheric effects, and measurement errors between years. We also describe a method for determining the most discriminative time window that allows high classification accuracies with limited data. We carried out tests of our approach with Landsat 8 time-series imagery from years 2013 to 2016 for classification of corn and cotton in the Harran Plain, and corn, cotton, and soybean in the Bismil Plain of Southeastern Turkey. In addition, we tested VDTW corn and soybean in Kansas, the US for 2017 and 2018 with the Harmonized Landsat Sentinel data. The VDTW method achieved 99.85% and 99.74% overall accuracies for the same and cross years, respectively with fewer training samples compared to other state-of-the-art approaches, i.e. spectral angle mapper (SAM), dynamic time warping (DTW), time-weighted DTW (TWDTW), random forest (RF), support vector machine (SVM) and deep long short-term memory (LSTM) methods. The proposed method could be expanded for other crop types and/or geographical areas.

Includes members:
  Member "deep learning in agriculture a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "road network and travel time extraction from multiple look angles with spacenet data")
  Member "optimal hyperparameters for deep lstm networks for sequence labeling tasks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking generalization of neural models a named entity recognition case study")

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.LG(Machine Learning)