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 "artificial intelligence strategies for national security and safety standards"):
  Abstract: Recent advances in artificial intelligence (AI) have lead to an explosion of multimedia applications (e.g., computer vision (CV) and natural language processing (NLP)) for different domains such as commercial, industrial, and intelligence. In particular, the use of AI applications in a national security environment is often problematic because the opaque nature of the systems leads to an inability for a human to understand how the results came about. A reliance on 'black boxes' to generate predictions and inform decisions is potentially disastrous. This paper explores how the application of standards during each stage of the development of an AI system deployed and used in a national security environment would help enable trust. Specifically, we focus on the standards outlined in Intelligence Community Directive 203 (Analytic Standards) to subject machine outputs to the same rigorous standards as analysis performed by humans.

Includes members:
  Member "model cards for model reporting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches")
  Member "headline generation learning from decomposed document titles":
    also connected through other hyperedges:
      (none)
  Member "a nutritional label for rankings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches")
  Member "blendcac a blockchain enabled decentralized capability based access control for iots":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sok of used cryptography in blockchain")
  Member "training augmentation with adversarial examples for robust speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving the interpretability of neural sentiment classifiers via data augmentation")
  Member "datasheets for datasets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches")
  Member "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences")
  Member "the dataset nutrition label a framework to drive higher data quality standards":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches")

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.CY(Computers and Society)