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: "how the experts do it assessing and explaining agent behaviors in real time strategy games"
  Abstract: How should an AI-based explanation system explain an agent's complex behavior to ordinary end users who have no background in AI? Answering this question is an active research area, for if an AI-based explanation system could effectively explain intelligent agents' behavior, it could enable the end users to understand, assess, and appropriately trust (or distrust) the agents attempting to help them. To provide insights into this question, we turned to human expert explainers in the real-time strategy domain, "shoutcaster", to understand (1) how they foraged in an evolving strategy game in real time, (2) how they assessed the players' behaviors, and (3) how they constructed pertinent and timely explanations out of their insights and delivered them to their audience. The results provided insights into shoutcasters' foraging strategies for gleaning information necessary to assess and explain the players; a characterization of the types of implicit questions shoutcasters answered; and implications for creating explanations by using the patterns

Connected through hyperedge HE-1 (induced by source paper "explanation in human ai systems a literature meta review synopsis of key ideas and publications and bibliography for explainable ai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "ted teaching ai to explain its decisions"
    - "explaining explanations an overview of interpretability of machine learning"
    - "deep learning a critical appraisal"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "explainable artificial intelligence xai the goodness criteria and the grasp ability test"
    - "interpretable explorable approximations of black box models"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"

Connected through hyperedge HE-2 (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"
    - "toward foraging for understanding of starcraft agents an empirical study"
    - "hybrid reward architecture for reinforcement learning"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "visualizing and understanding atari agents"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-3 (induced by source paper "automated let s play commentary"):
  with members:
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "universal sentence encoder"
    - "towards automated let s play commentary"
    - "procedural content generation via machine learning pcgml"
    - "resnet in resnet generalizing residual architectures"
    - "empirical evaluation of rectified activations in convolutional network"
    - "taking a deeper look at pedestrians"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "let s make it personal a challenge in personalizing medical inter human communication"):
  with members:
    - "explanation in artificial intelligence insights from the social sciences"

Connected through hyperedge HE-5 (induced by source paper "player experience extraction from gameplay video"):
  with members:
    - "procedural content generation via machine learning pcgml"
    - "evaluating singleplayer and multiplayer in human computation games"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "two stream convolutional networks for action recognition in videos"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

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)