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 referee a neural symbolic framework for enhancing geospatial semantic segmentation"):
  Abstract: Understanding why machine learning algorithms may fail is usually the task of the human expert that uses domain knowledge and contextual information to discover systematic shortcomings in either the data or the algorithm. In this paper, we propose a semantic referee, which is able to extract qualitative features of the errors emerging from deep machine learning frameworks and suggest corrections. The semantic referee relies on ontological reasoning about spatial knowledge in order to characterize errors in terms of their spatial relations with the environment. Using semantics, the reasoner interacts with the learning algorithm as a supervisor. In this paper, the proposed method of the interaction between a neural network classifier and a semantic referee shows how to improve the performance of semantic segmentation for satellite imagery data.

Includes members:
  Member "location augmentation for cnn":
    also connected through other hyperedges:
      (none)
  Member "an intriguing failing of convolutional neural networks and the coordconv solution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "deep learning in remote sensing a review":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic multicontext segmentation of remote sensing images based on convolutional networks")
  Member "comprehensive survey of deep learning in remote sensing theories tools and challenges for the community":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalizing deep models for overhead image segmentation through getis ord gi pooling")
  Member "explaining trained neural networks with semantic web technologies first steps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "knowledge infused learning k il towards deep incorporation of knowledge in deep learning")
  Member "neural symbolic learning and reasoning a survey and interpretation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "relating input concepts to convolutional neural network decisions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "causal learning and explanation of deep neural networks via autoencoded activations")
  Member "what does explainable ai really mean a new conceptualization of perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "directions for explainable knowledge enabled systems")

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.AI(Artificial Intelligence)