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: "non compositional term dependence for information retrieval"
  Abstract: Modelling term dependence in IR aims to identify co-occurring terms that are too heavily dependent on each other to be treated as a bag of words, and to adapt the indexing and ranking accordingly. Dependent terms are predominantly identified using lexical frequency statistics, assuming that (a) if terms co-occur often enough in some corpus, they are semantically dependent; (b) the more often they co-occur, the more semantically dependent they are. This assumption is not always correct: the frequency of co-occurring terms can be separate from the strength of their semantic dependence. E.g. "red tape" might be overall less frequent than "tape measure" in some corpus, but this does not mean that "red"+"tape" are less dependent than "tape"+"measure". This is especially the case for non-compositional phrases, i.e. phrases whose meaning cannot be composed from the individual meanings of their terms (such as the phrase "red tape" meaning bureaucracy). Motivated by this lack of distinction between the frequency and strength of term dependence in IR, we present a principled approach for handling term dependence in queries, using both lexical frequency and semantic evidence. We focus on non-compositional phrases, extending a recent unsupervised model for their detection [21] to IR. Our approach, integrated into ranking using Markov Random Fields [31], yields effectiveness gains over competitive TREC baselines, showing that there is still room for improvement in the very well-studied area of term dependence in IR.

Connected through hyperedge HE-1 (induced by source paper "on the compositionality prediction of noun phrases using poincar e embeddings"):
  with members:
    - "every child should have parents a taxonomy refinement algorithm based on hyperbolic term embeddings"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "hyperbolic image embeddings"
    - "poincar e embeddings for learning hierarchical representations"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "contextually propagated term weights for document representation"):
  with members:
    - "neural speed reading with structural jump lstm"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "to phrase or not to phrase impact of user versus system term dependence upon retrieval"):
  with members:
    - "a study of metrics of distance and correlation between ranked lists for compositionality detection"
    - "an ir based evaluation framework for web search query segmentation"

Connected through hyperedge HE-4 (induced by source paper "a study of metrics of distance and correlation between ranked lists for compositionality detection"):
  with members:
    - "rhetorical relations for information retrieval"
    - "part of speech based term weighting for information retrieval"
    - "maximal rank correlation"
    - "maximal rank correlation"
    - "a weighted correlation index for rankings with ties"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "neural information retrieval a literature review"):
  with members:
    - "lstm a search space odyssey"
    - "tweet2vec character based distributed representations for social media"
    - "using word embeddings for automatic query expansion"
    - "representing documents and queries as sets of word embedded vectors for information retrieval"
    - "using word embeddings in twitter election classification"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a study of matchpyramid models on ad hoc retrieval"
    - "toward a deep neural approach for knowledge based ir"

Connected through hyperedge HE-6 (induced by source paper "a study of factuality objectivity and relevance three desiderata in large scale information retrieval"):
  with members:
    - "rhetorical relations for information retrieval"
    - "exploiting the bipartite structure of entity grids for document coherence and retrieval"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "an improved multileaving algorithm for online ranker evaluation"
    - "entropy and graph based modelling of document coherence using discourse entities an application"
    - "efficient and effective spam filtering and re ranking for large web datasets"

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)