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: "solving the black box problem a normative framework for explainable artificial intelligence"
  Abstract: Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. The Explainable Artificial Intelligence research program aims to develop analytic techniques with which to render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques' explanatory success. The aim of the present discussion is to develop such a framework, while paying particular attention to different stakeholders' distinct explanatory requirements. Building on an analysis of 'opacity' from philosophy of science, this framework is modeled after David Marr's influential account of explanation in cognitive science. Thus, the framework distinguishes between the different questions that might be asked about an opaque computing system, and specifies the general way in which these questions should be answered. By applying this normative framework to current techniques such as input heatmapping, feature-detector identification, and diagnostic classification, it will be possible to determine whether and to what extent the Black Box Problem can be solved.

Connected through hyperedge HE-1 (induced by source paper "using explainable artificial intelligence to increase trust in computer vision"):
  with members:
    - "do you comply with ai personalized explanations of learning algorithms and their impact on employees compliance behavior"
    - "ai skin skin disease recognition based on self learning and wide data collection through a closed loop framework"

Connected through hyperedge HE-2 (induced by source paper "does alphago actually play go concerning the state space of artificial intelligence"):
  with members:
    - "playing atari with deep reinforcement learning"
    - "the symbol grounding problem"

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.GL(General Literature)