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: "evaluating personal assistants on mobile devices"
  Abstract: The iPhone was introduced only a decade ago in 2007 but has fundamentally changed the way we interact with online information. Mobile devices differ radically from classic command-based and point-and-click user interfaces, now allowing for gesture-based interaction using fine-grained touch and swipe signals. Due to the rapid growth in the use of voice-controlled intelligent personal assistants on mobile devices, such as Microsoft's Cortana, Google Now, and Apple's Siri, mobile devices have become personal, allowing us to be online all the time, and assist us in any task, both in work and in our daily lives, making context a crucial factor to consider. #R##N#Mobile usage is now exceeding desktop usage, and is still growing at a rapid rate, yet our main ways of training and evaluating personal assistants are still based on (and framed in) classical desktop interactions, focusing on explicit queries, clicks, and dwell time spent. However, modern user interaction with mobile devices is radically different due to touch screens with a gesture- and voice-based control and the varying context of use, e.g., in a car, by bike, often invalidating the assumptions underlying today's user satisfaction evaluation. #R##N#There is an urgent need to understand voice- and gesture-based interaction, taking all interaction signals and context into account in appropriate ways. We propose a research agenda for developing methods to evaluate and improve context-aware user satisfaction with mobile interactions using gesture-based signals at scale.

Connected through hyperedge HE-1 (induced by source paper "a click sequence model for web search"):
  with members:
    - "constructing an interaction behavior model for web image search"
    - "sequential click prediction for sponsored search with recurrent neural networks"
    - "neural machine translation by jointly learning to align and translate"
    - "how to construct deep recurrent neural networks"
    - "sequence transduction with recurrent neural networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "a survey investigating usage of virtual personal assistants"):
  with members:
    - "comparing speech and keyboard text entry for short messages in two languages on touchscreen phones"
    - "very deep multilingual convolutional neural networks for lvcsr"

Connected through hyperedge HE-3 (induced by source paper "attentive memory networks efficient machine reading for conversational search"):
  with members:
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "tracking the world state with recurrent entity networks"
    - "key value memory networks for directly reading documents"
    - "dynamic memory networks for visual and textual question answering"
    - "learning end to end goal oriented dialog"
    - "recurrent memory networks for language modeling"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"

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)