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 "machine learning for the geosciences challenges and opportunities"):
  Abstract: Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data, machine learning (ML) -- that has been widely successful in commercial domains -- offers immense potential to contribute to problems in geosciences. However, problems in geosciences have several unique challenges that are seldom found in traditional applications, requiring novel problem formulations and methodologies in machine learning. This article introduces researchers in the machine learning (ML) community to these challenges offered by geoscience problems and the opportunities that exist for advancing both machine learning and geosciences. We first highlight typical sources of geoscience data and describe their properties that make it challenging to use traditional machine learning techniques. We then describe some of the common categories of geoscience problems where machine learning can play a role, and discuss some of the existing efforts and promising directions for methodological development in machine learning. We conclude by discussing some of the emerging research themes in machine learning that are applicable across all problems in the geosciences, and the importance of a deep collaboration between machine learning and geosciences for synergistic advancements in both disciplines.

Includes members:
  Member "spatio temporal data mining a survey of problems and methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi supervised classification using attention based regularization on coarse resolution data")
  Member "deepsd generating high resolution climate change projections through single image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fine grained urban flow inference")
  Member "extremeweather a large scale climate dataset for semi supervised detection localization and understanding of extreme weather events":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tackling climate change with machine learning")
  Member "label free supervision of neural networks with physics and domain knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "application of deep convolutional neural networks for detecting extreme weather in climate datasets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tackling climate change with machine learning")
  Member "transfer learning from deep features for remote sensing and poverty mapping":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bigearthnet dataset with a new class nomenclature for remote sensing image understanding")
  Member "deepsat a learning framework for satellite imagery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis")
  Member "delving deep into rectifiers surpassing human level performance on imagenet classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)