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: "part of speech based term weighting for information retrieval"
  Abstract: Automatic language processing tools typically assign to terms so-called weights corresponding to the contribution of terms to information content. Traditionally, term weights are computed from lexical statistics, e.g., term frequencies. We propose a new type of term weight that is computed from part of speech (POS) n-gram statistics. The proposed POS-based term weight represents how informative a term is in general, based on the POS contexts in which it generally occurs in language. We suggest five different computations of POS-based term weights by extending existing statistical approximations of term information measures. We apply these POS-based term weights to information retrieval, by integrating them into the model that matches documents to queries. Experiments with two TREC collections and 300 queries, using TF-IDF & BM25 as baselines, show that integrating our POS-based term weights to retrieval always leads to gains (up to +33.7% from the baseline). Additional experiments with a different retrieval model as baseline (Language Model with Dirichlet priors smoothing) and our best performing POS-based term weight, show retrieval gains always and consistently across the whole smoothing range of the baseline.

Connected through hyperedge HE-1 (induced by source paper "on the estimation and use of statistical modelling in information retrieval"):
  with members:
    - "social resilience in online communities the autopsy of friendster"
    - "vocabulary growth in collaborative tagging systems"
    - "collaborative thesaurus tagging the wikipedia way"
    - "the structure of broad topics on the web"
    - "search in power law networks"

Connected through hyperedge HE-2 (induced by source paper "dependencies formalising semantic catenae for information retrieval"):
  with members:
    - "fixed versus dynamic co occurrence windows in textrank term weights for information retrieval"
    - "preliminary experiments using subjective logic for the polyrepresentation of information needs"
    - "rhetorical relations for information retrieval"
    - "time series adaptive estimation of vaccination uptake using web search queries"
    - "learning to generate compositional color descriptions"
    - "adaptive distributional extensions to dfr ranking"
    - "exploiting the bipartite structure of entity grids for document coherence and retrieval"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "a study of metrics of distance and correlation between ranked lists for compositionality detection"):
  with members:
    - "rhetorical relations for information retrieval"
    - "maximal rank correlation"
    - "maximal rank correlation"
    - "non compositional term dependence for information retrieval"
    - "a weighted correlation index for rankings with ties"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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)