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: "improving query expansion using wordnet"
  Abstract: This study proposes a new way of using WordNet for Query Expansion (QE). We choose candidate expansion terms, as usual, from a set of pseudo relevant documents; however, the usefulness of these terms is measured based on their definitions provided in a hand-crafted lexical resource like WordNet. Experiments with a number of standard TREC collections show that this method outperforms existing WordNet based methods. It also compares favorably with established QE methods such as KLD and RM3. Leveraging earlier work in which a combination of QE methods was found to outperform each individual method (as well as other well-known QE methods), we next propose a combination-based QE method that takes into account three different aspects of a candidate expansion term's usefulness: (i) its distribution in the pseudo relevant documents and in the target corpus, (ii) its statistical association with query terms, and (iii) its semantic relation with the query, as determined by the overlap between the WordNet definitions of the term and query terms. This combination of diverse sources of information appears to work well on a number of test collections, viz., TREC123, TREC5, TREC678, TREC robust new and TREC910 collections, and yields significant improvements over competing methods on most of these collections.

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"
    - "query expansion using term distribution and term association"

Connected through hyperedge HE-2 (induced by source paper "spatial search strategies for open government data a systematic comparison"):
  with members:
    - "query expansion techniques for information retrieval a survey"
    - "a consumer focused open data platform"

Connected through hyperedge HE-3 (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"
    - "exploring query categorisation for query expansion a study"

Connected through hyperedge HE-4 (induced by source paper "dsrim a deep neural information retrieval model enhanced by a knowledge resource driven representation of documents"):
  with members:
    - "neural ranking models with weak supervision"
    - "a deep relevance matching model for ad hoc retrieval"
    - "toward a deep neural approach for knowledge based ir"
    - "a dual embedding space model for document ranking"
    - "text matching as image recognition"
    - "convolutional neural network architectures for matching natural language sentences"
    - "distributed representations of sentences and documents"
    - "retrofitting word vectors to semantic lexicons"

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)