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: "exploring query categorisation for query expansion a study"
  Abstract: The vocabulary mismatch problem is one of the important challenges facing traditional keyword-based Information Retrieval Systems. The aim of query expansion (QE) is to reduce this query-document mismatch by adding related or synonymous words or phrases to the query. #R##N#Several existing query expansion algorithms have proved their merit, but they are not uniformly beneficial for all kinds of queries. Our long-term goal is to formulate methods for applying QE techniques tailored to individual queries, rather than applying the same general QE method to all queries. As an initial step, we have proposed a taxonomy of query classes (from a QE perspective) in this report. We have discussed the properties of each query class with examples. We have also discussed some QE strategies that might be effective for each query category. #R##N#In future work, we intend to test the proposed techniques using standard datasets, and to explore automatic query categorisation methods.

Connected through hyperedge HE-1 (induced by source paper "query expansion techniques for information retrieval a survey"):
  with members:
    - "just an update on pming distance for web based semantic similarity in artificial intelligence and data mining"
    - "using word embeddings for automatic query expansion"
    - "query expansion with locally trained word embeddings"
    - "query expansion via structural motifs in wikipedia graph"
    - "query expansion based on crowd knowledge for code search"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "query expansion using term distribution and term association"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "a new approach for query expansion using wikipedia and wordnet"):
  with members:
    - "an analysis of hierarchical text classification using word embeddings"
    - "improving query expansion using wordnet"

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)