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: "mobile information retrieval"
  Abstract: Mobile Information Retrieval (Mobile IR) is a relatively recent branch of Information Retrieval (IR) that is concerned with enabling users to carry out, using a mobile device, all the classical IR operations that they were used to carry out on a desktop. This includes finding content available on local repositories or on the web in response to a user query, interacting with the system in an explicit or implicit way, reformulate the query and/or visualise the content of the retrieved documents, as well as providing relevance judgments to improve the retrieval process. #R##N#This book is structured as follows. Chapter 2 provides a very brief overview of IR and of Mobile IR, briefly outlining what in Mobile IR is different from IR. Chapter 3 provides the foundations of Mobile IR, looking at the characteristics of mobile devices and what they bring to IR, but also looking at how the concept of relevance changed from standard IR to Mobile IR. Chapter 4 presents an overview of the document collections that are searchable by a Mobile IR system, and that are somehow different from classical IR ones; available for experimentation, including collections of data that have become complementary to Mobile IR. Similarly, Chapter 5 reviews mobile information needs studies and users log analysis. Chapter 6 reviews studies aimed at adapting and improving the users interface to the needs of Mobile IR. Chapter 7, instead, reviews work on context awareness, which studies the many aspects of the user context that Mobile IR employs. Chapter 8 reviews some of evaluation work done in Mobile IR, highlighting the distinctions with classical IR from the perspectives of two main IR evaluation methodologies: users studies and test collections. Finally, Chapter 9 reports the conclusions of this review, highlighting briefly some trends in Mobile IR that we believe will drive research in the next few years.

Connected through hyperedge HE-1 (induced by source paper "a tool for conducting user studies on mobile devices"):
  with members:
    - "understanding mobile search task relevance and user behaviour in context"
    - "asking clarifying questions in open domain information seeking conversations"
    - "target apps selection towards a unified search framework for mobile devices"

Connected through hyperedge HE-2 (induced by source paper "understanding mobile search task relevance and user behaviour in context"):
  with members:
    - "target apps selection towards a unified search framework for mobile devices"

Connected through hyperedge HE-3 (induced by source paper "target apps selection towards a unified search framework for mobile devices"):
  with members:
    - "neural ranking models with weak supervision"
    - "neural ranking models with multiple document fields"
    - "anticipating information needs based on check in activity"
    - "personalized ranking for context aware venue suggestion"
    - "a deep relevance matching model for ad hoc retrieval"
    - "deep learning for answer sentence selection"

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)