human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "explaining trained neural networks with semantic web technologies first steps"
  Abstract: The ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains. In this paper, we provide a conceptual approach that leverages such data in order to explain the input-output behavior of trained artificial neural networks. We apply existing Semantic Web technologies in order to provide an experimental proof of concept.

Connected through hyperedge HE-1 (induced by source paper "knowledge infused learning k il towards deep incorporation of knowledge in deep learning"):
  with members:
    - "infusing domain knowledge in ai based black box models for better explainability with application in bankruptcy prediction"
    - "ernie enhanced representation through knowledge integration"
    - "predictive analysis on twitter techniques and applications"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "modeling islamist extremist communications on social media using contextual dimensions religion ideology and hate"
    - "k bert enabling language representation with knowledge graph"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "ai reasoning systems pac and applied methods"

Connected through hyperedge HE-2 (induced by source paper "a boxology of design patterns for hybrid learning and reasoning systems"):
  with members:
    - "model free model based and general intelligence"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "reasoning over rdf knowledge bases using deep learning"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "knowledge based transfer learning explanation"
    - "differentiable learning of logical rules for knowledge base reasoning"

Connected through hyperedge HE-3 (induced by source paper "semantic referee a neural symbolic framework for enhancing geospatial semantic segmentation"):
  with members:
    - "location augmentation for cnn"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "deep learning in remote sensing a review"
    - "comprehensive survey of deep learning in remote sensing theories tools and challenges for the community"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "relating input concepts to convolutional neural network decisions"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "how a general purpose commonsense ontology can improve performance of learning based image retrieval"

Question: Please tell me which class the center node 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)