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: "anticipating information needs based on check in activity"
  Abstract: In this work we address the development of a smart personal assistant that is capable of anticipating a user's information needs based on a novel type of context: the person's activity inferred from her check-in records on a location-based social network. Our main contribution is a method that translates a check-in activity into an information need, which is in turn addressed with an appropriate information card. This task is challenging because of the large number of possible activities and related information needs, which need to be addressed in a mobile dashboard that is limited in size. Our approach considers each possible activity that might follow after the last (and already finished) activity, and selects the top information cards such that they maximize the likelihood of satisfying the user's information needs for all possible future scenarios. The proposed models also incorporate knowledge about the temporal dynamics of information needs. Using a combination of historical check-in data and manual assessments collected via crowdsourcing, we show experimentally the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "asking clarifying questions in open domain information seeking conversations"):
  with members:
    - "answer based adversarial training for generating clarification questions"
    - "investigating the successes and failures of bert for passage re ranking"
    - "a deep look into neural ranking models for information retrieval"
    - "target apps selection towards a unified search framework for mobile devices"
    - "learning to ask questions in open domain conversational systems with typed decoders"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"

Connected through hyperedge HE-2 (induced by source paper "target apps selection towards a unified search framework for mobile devices"):
  with members:
    - "neural ranking models with weak supervision"
    - "mobile information retrieval"
    - "neural ranking models with multiple document fields"
    - "personalized ranking for context aware venue suggestion"
    - "a deep relevance matching model for ad hoc retrieval"
    - "deep learning for answer sentence selection"

Connected through hyperedge HE-3 (induced by source paper "reply with proactive recommendation of email attachments"):
  with members:
    - "pyndri a python interface to the indri search engine"
    - "smart reply automated response suggestion for email"
    - "adam a method for stochastic optimization"

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)