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: "uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks"
  Abstract: Background:#R##N#We participated in three of the protein-protein interaction subtasks of the Second BioCreative Challenge: classification of abstracts relevant for protein-protein interaction (interaction article subtask [IAS]), discovery of protein pairs (interaction pair subtask [IPS]), and identification of text passages characterizing protein interaction (interaction sentences subtask [ISS]) in full-text documents. We approached the abstract classification task with a novel, lightweight linear model inspired by spam detection techniques, as well as an uncertainty-based integration scheme. We also used a support vector machine and singular value decomposition on the same features for comparison purposes. Our approach to the full-text subtasks (protein pair and passage identification) includes a feature expansion method based on word proximity networks.

Connected through hyperedge HE-1 (induced by source paper "distance closures on complex networks"):
  with members:
    - "semi metric networks for recommender systems"
    - "sustaining the internet with hyperbolic mapping"
    - "semi metric behavior in document networks and its application to recommendation systems"
    - "mining the web for synonyms pmi ir versus lsa on toefl"
    - "all pairs shortest paths using bridging sets and rectangular matrix multiplication"
    - "biologically motivated distributed designs for adaptive knowledge management"

Connected through hyperedge HE-2 (induced by source paper "semi metric networks for recommender systems"):
  with members:
    - "semi metric behavior in document networks and its application to recommendation systems"
    - "mining the web for synonyms pmi ir versus lsa on toefl"
    - "all pairs shortest paths using bridging sets and rectangular matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "adaptive spam detection inspired by a cross regulation model of immune dynamics a study of concept drift"):
  with members:
    - "an experimental comparison of naive bayesian and keyword based anti spam filtering with personal e mail messages"

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)