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 techniques for information retrieval a survey"
  Abstract: Abstract   With the ever increasing size of the web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. Query Expansion (QE) plays a crucial role in improving searches on the Internet. Here, the user’s initial query is reformulated by adding additional meaningful terms with similar significance. QE – as part of information retrieval (IR) – has long attracted researchers’ attention. It has become very influential in the field of personalized social document, question answering, cross-language IR, information filtering and multimedia IR. Research in QE has gained further prominence because of IR dedicated conferences such as TREC (Text Information Retrieval Conference) and CLEF (Conference and Labs of the Evaluation Forum). This paper surveys QE techniques in IR from 1960 to 2017 with respect to core techniques, data sources used, weighting and ranking methodologies, user participation and applications – bringing out similarities and differences.

Connected through hyperedge HE-1 (induced by source paper "semantically enriched search engine for geoportals a case study with arcgis online"):
  with members:
    - "multi scale representation learning for spatial feature distributions using grid cells"

Connected through hyperedge HE-2 (induced by source paper "knowledge based conversational search"):
  with members:
    - "message passing for complex question answering over knowledge graphs"
    - "open data chatbot"
    - "towards a question answering system over the semantic web"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "interpretation of natural language rules in conversational machine reading"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "dialogue generation from imitation learning to inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "deep metric learning using similarities from nonlinear rank approximations"):
  with members:
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "deep metric learning with hierarchical triplet loss"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "identity mappings in deep residual networks"
    - "deep metric learning via facility location"
    - "local similarity aware deep feature embedding"
    - "hard aware deeply cascaded embedding"

Connected through hyperedge HE-4 (induced by source paper "a novel model for query expansion using pseudo relevant web knowledge"):
  with members:
    - "a new approach for query expansion using wikipedia and wordnet"
    - "using word embeddings for automatic query expansion"

Connected through hyperedge HE-5 (induced by source paper "spatial search strategies for open government data a systematic comparison"):
  with members:
    - "a consumer focused open data platform"
    - "improving query expansion using wordnet"

Connected through hyperedge HE-6 (induced by source paper "answering science exam questions using query rewriting with background knowledge"):
  with members:
    - "improving question answering by commonsense based pre training"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
    - "know what you don t know unanswerable questions for squad"
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "improving natural language inference using external knowledge in the science questions domain"
    - "improving machine reading comprehension with general reading strategies"
    - "ncrf an open source neural sequence labeling toolkit"

Connected through hyperedge HE-7 (induced by source paper "xu an automated query expansion and optimization tool"):
  with members:
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "automated query expansion using high dimensional clustering"):
  with members:
    - "efficient estimation of word representations in vector space"

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)