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: "query expansion using term distribution and term association"
  Abstract: Good term selection is an important issue for an automatic query expansion (AQE) technique. AQE techniques that select expansion terms from the target corpus usually do so in one of two ways. Distribution based term selection compares the distribution of a term in the (pseudo) relevant documents with that in the whole corpus / random distribution. Two well-known distribution-based methods are based on Kullback-Leibler Divergence (KLD) and Bose-Einstein statistics (Bo1). Association based term selection, on the other hand, uses information about how a candidate term co-occurs with the original query terms. Local Context Analysis (LCA) and Relevance-based Language Model (RM3) are examples of association-based methods. Our goal in this study is to investigate how these two classes of methods may be combined to improve retrieval effectiveness. We propose the following combination-based approach. Candidate expansion terms are first obtained using a distribution based method. This set is then refined based on the strength of the association of terms with the original query terms. We test our methods on 11 TREC collections. The proposed combinations generally yield better results than each individual method, as well as other state-of-the-art AQE approaches. En route to our primary goal, we also propose some modifications to LCA and Bo1 which lead to improved performance.

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"
    - "exploring query categorisation for query expansion a study"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "improving query expansion using wordnet"):
  with members:
    - "comparing and combining methods for automatic query expansion"

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)