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: "toward foraging for understanding of starcraft agents an empirical study"
  Abstract: Assessing and understanding intelligent agents is a difficult task for users that lack an AI background. A relatively new area, called "Explainable AI," is emerging to help address this problem, but little is known about how users would forage through information an explanation system might offer. To inform the development of Explainable AI systems, we conducted a formative study, using the lens of Information Foraging Theory, into how experienced users foraged in the domain of StarCraft to assess an agent. Our results showed that participants faced difficult foraging problems. These foraging problems caused participants to entirely miss events that were important to them, reluctantly choose to ignore actions they did not want to ignore, and bear high cognitive, navigation, and information costs to access the information they needed.

Connected through hyperedge HE-1 (induced by source paper "explaining reinforcement learning to mere mortals an empirical study"):
  with members:
    - "metrics for explainable ai challenges and prospects"
    - "sanity checks for saliency maps"
    - "hybrid reward architecture for reinforcement learning"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "how the experts do it assessing and explaining agent behaviors in real time strategy games"
    - "visualizing and understanding atari agents"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-2 (induced by source paper "a survey of evaluation methods and measures for interpretable machine learning"):
  with members:
    - "visual interpretability for deep learning a survey"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "methods for interpreting and understanding deep neural networks"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "explanation in artificial intelligence insights from the social sciences"
    - "understanding hidden memories of recurrent neural networks"

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.HC(Human-Computer Interaction)