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: "two stage document length normalization for information retrieval"
  Abstract: The standard approach for term frequency normalization is based only on the document length. However, it does not distinguish the verbosity from the scope, these being the two main factors determining the document length. Because the verbosity and scope have largely different effects on the increase in term frequency, the standard approach can easily suffer from insufficient or excessive penalization depending on the specific type of long document. To overcome these problems, this paper proposes two-stage normalization by performing verbosity and scope normalization separately, and by employing different penalization functions. In verbosity normalization, each document is pre-normalized by dividing the term frequency by the verbosity of the document. In scope normalization, an existing retrieval model is applied in a straightforward manner to the pre-normalized document, finally leading us to formulate our proposed verbosity normalized (VN) retrieval model. Experimental results carried out on standard TREC collections demonstrate that the VN model leads to marginal but statistically significant improvements over standard retrieval models.

Connected through hyperedge HE-1 (induced by source paper "learning to weight for text classification"):
  with members:
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "balancing between over weighting and under weighting in supervised term weighting"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "credibility adjusted term frequency a supervised term weighting scheme for sentiment analysis and text classification"
    - "distributed representations of words and phrases and their compositionality"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-2 (induced by source paper "modeling diverse relevance patterns in ad hoc retrieval"):
  with members:
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "neural models for information retrieval"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a study of matchpyramid models on ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "match srnn modeling the recursive matching structure with spatial rnn"
    - "convolutional neural network architectures for matching natural language sentences"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "a polya urn document language model for improved information retrieval"):
  with members:
    - "improving term frequency normalization for multi topical documents and application to language modeling approaches"

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)