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: "bias amplification in artificial intelligence systems"
  Abstract: As Artificial Intelligence (AI) technologies proliferate, concern has centered around the long-term dangers of job loss or threats of machines causing harm to humans. All of this concern, however, detracts from the more pertinent and already existing threats posed by AI today: its ability to amplify bias found in training datasets, and swiftly impact marginalized populations at scale. Government and public sector institutions have a responsibility to citizens to establish a dialogue with technology developers and release thoughtful policy around data standards to ensure diverse representation in datasets to prevent bias amplification and ensure that AI systems are built with inclusion in mind.

Connected through hyperedge HE-1 (induced by source paper "unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "early identification of important patents design and validation of citation network metrics"
    - "a review of theory and practice in scientometrics"
    - "a review of the literature on citation impact indicators"
    - "methods for the generation of normalized citation impact scores in bibliometrics which method best reflects the judgements of experts"
    - "coauthorship and citation patterns in the physical review"
    - "the use of percentiles and percentile rank classes in the analysis of bibliometric data opportunities and limits"
    - "quantifying the influence of scientists and their publications distinguishing between prestige and popularity"

Connected through hyperedge HE-2 (induced by source paper "implications of computer vision driven assistive technologies towards individuals with visual impairment"):
  with members:
    - "human centered artificial intelligence and machine learning"
    - "capuchin causal database repair for algorithmic fairness"
    - "ai fairness for people with disabilities point of view"

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)