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: "incorporating clicks attention and satisfaction into a search engine result page evaluation model"
  Abstract: Modern search engine result pages often provide immediate value to users and organize information in such a way that it is easy to navigate. The core ranking function contributes to this and so do result snippets, smart organization of result blocks and extensive use of one-box answers or side panels. While they are useful to the user and help search engines to stand out, such features present two big challenges for evaluation. First, the presence of such elements on a search engine result page (SERP) may lead to the absence of clicks, which is, however, not related to dissatisfaction, so-called 'good abandonments.' Second, the non-linear layout and visual difference of SERP items may lead to non-trivial patterns of user attention, which is not captured by existing evaluation metrics.   In this paper we propose a model of user behavior on a SERP that jointly captures click behavior, user attention and satisfaction, the CAS model, and demonstrate that it gives more accurate predictions of user actions and self-reported satisfaction than existing models based on clicks alone. We use the CAS model to build a novel evaluation metric that can be applied to non-linear SERP layouts and that can account for the utility that users obtain directly on a SERP. We demonstrate that this metric shows better agreement with user-reported satisfaction than conventional evaluation metrics.

Connected through hyperedge HE-1 (induced by source paper "ranking for relevance and display preferences in complex presentation layouts"):
  with members:
    - "why people search for images using web search engines"
    - "on application of learning to rank for e commerce search"
    - "large scale validation of counterfactual learning methods a test bed"
    - "deep reinforcement learning with double q learning"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "introducing letor 4 0 datasets"

Connected through hyperedge HE-2 (induced by source paper "prosody modifications for question answering in voice only settings"):
  with members:
    - "squad 100 000 questions for machine comprehension of text"
    - "ms marco a human generated machine reading comprehension dataset"
    - "wavenet a generative model for raw audio"

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.IR(Information Retrieval)