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: "the partial evaluation approach to information personalization"
  Abstract: Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology — PIPE (‘Personalization is Partial Evaluation’) — for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a conceptual level and outline representational choices. We present two application case studies that use PIPE for personalizing web sites and describe how PIPE suggests a novel evaluation criterion for information system designs. Finally, w e mention several fundamental implications of adopting the PIPE model for personalization and when it is (and is not) applicable.

Connected through hyperedge HE-1 (induced by source paper "the traits of the personable"):
  with members:
    - "a personalized system for conversational recommendations"
    - "staging transformations for multimodal web interaction management"
    - "explaining scenarios for information personalization"
    - "mixed initiative interaction mixed computation"

Connected through hyperedge HE-2 (induced by source paper "recommendation and personalization a survey"):
  with members:
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "the traits of the personable"
    - "evaluating recommendation algorithms by graph analysis"
    - "content based book recommending using learning for text categorization"

Connected through hyperedge HE-3 (induced by source paper "a connection centric survey of recommender systems research"):
  with members:
    - "the traits of the personable"
    - "evaluating recommendation algorithms by graph analysis"
    - "content based book recommending using learning for text categorization"

Connected through hyperedge HE-4 (induced by source paper "explaining scenarios for information personalization"):
  with members:
    - "mixed initiative interaction mixed computation"
    - "rerepresenting and restructuring domain theories a constructive induction approach"

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)