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 "land cover classification via multitemporal spatial data by deep recurrent neural networks"):
  Abstract: Nowadays, modern earth observation programs produce huge volumes of satellite images time series that can be useful to monitor geographical areas through time. How to efficiently analyze such a kind of information is still an open question in the remote sensing field. Recently, deep learning methods proved suitable to deal with remote sensing data mainly for scene classification(i.e., convolutional neural networks on single images) while only very few studies exist involving temporal deep learning approaches [i.e., recurrent neural networks (RNNs)] to deal with remote sensing time series. In this letter, we evaluate the ability of RNNs, in particular, the long short-term memory (LSTM) model, to perform land cover classification considering multitemporal spatial data derived from a time series of satellite images. We carried out experiments on two different data sets considering both pixel-based and object-based classifications. The obtained results show that RNNs are competitive compared with the state-of-the-art classifiers, and may outperform classical approaches in the presence of low represented and/or highly mixed classes. We also show that the alternative feature representation generated by LSTM can improve the performances of standard classifiers.

Includes members:
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "conditional image generation with pixelcnn decoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic latent residual video prediction")
  Member "assessing the ability of lstms to learn syntax sensitive dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "rmsprop and equilibrated adaptive learning rates for non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "domain independent dominance of adaptive methods")
  Member "speech recognition with deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "representation learning a review and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nestedvae isolating common factors via weak supervision")

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)