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: "an empirical study on display ad impression viewability measurements"
  Abstract: Display advertising normally charges advertisers for every single ad impression. Specifically, if an ad in a webpage has been loaded in the browser, an ad impression is counted. However, due to the position and size of the ad slot, lots of ads are actually not viewed but still measured as impressions and charged. These fraud ad impressions indeed undermine the efficacy of display advertising. A perfect ad impression viewability measurement should match what the user has really viewed with a short memory. In this paper, we conduct extensive investigations on display ad impression viewability measurements on dimensions of ad creative displayed pixel percentage and exposure time to find which measurement provides the most accurate ad impression counting. The empirical results show that the most accurate measurement counts one ad impression if more than 75% of the ad creative pixels have been exposed for at least 2 continuous seconds.

Connected through hyperedge HE-1 (induced by source paper "display advertising with real time bidding rtb and behavioural targeting"):
  with members:
    - "neural feature embedding for user response prediction in real time bidding rtb"
    - "optimal real time bidding frameworks discussion"
    - "product based neural networks for user response prediction"
    - "lift based bidding in ad selection"
    - "statistical arbitrage mining for display advertising"
    - "real time bidding benchmarking with ipinyou dataset"
    - "neural machine translation by jointly learning to align and translate"
    - "real time bid optimization with smooth budget delivery in online advertising"

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)