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: "improving data quality through deep learning and statistical models"
  Abstract: Traditional data quality control methods are based on users’ experience or previously established business rules, and this limits performance in addition to being a very time consuming process with lower than desirable accuracy. Utilizing deep learning, we can leverage computing resources and advanced techniques to overcome these challenges and provide greater value to users.

Connected through hyperedge HE-1 (induced by source paper "understanding artificial intelligence ethics and safety"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "model cards for model reporting"
    - "a framework for understanding unintended consequences of machine learning"
    - "spectrum data poisoning with adversarial deep learning"
    - "explaining explanations in ai"
    - "problem formulation and fairness"

Connected through hyperedge HE-2 (induced by source paper "benchmarking contemporary deep learning hardware and frameworks a survey of qualitative metrics"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "bigdl a distributed deep learning framework for big data"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "chainermn scalable distributed deep learning framework"
    - "deep learning on fpgas past present and future"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow a system for large scale machine learning"
    - "blocks and fuel frameworks for deep learning"

Connected through hyperedge HE-3 (induced by source paper "benchmarking deep learning hardware and frameworks qualitative metrics"):
  with members:
    - "bigdl a distributed deep learning framework for big data"
    - "chainermn scalable distributed deep learning framework"
    - "deep learning on fpgas past present and future"
    - "theano a python framework for fast computation of mathematical expressions"
    - "blocks and fuel frameworks for deep learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "purine a bi graph based deep learning framework"
    - "caffe convolutional architecture for fast feature embedding"

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)