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: "exploiting the bipartite structure of entity grids for document coherence and retrieval"
  Abstract: Document coherence describes how much sense text makes in terms of its logical organisation and discourse flow. Even though coherence is a relatively difficult notion to quantify precisely, it can be approximated automatically. This type of coherence modelling is not only interesting in itself, but also useful for a number of other text processing tasks, including Information Retrieval (IR), where adjusting the ranking of documents according to both their relevance and their coherence has been shown to increase retrieval effectiveness [34,37]. #R##N#The state of the art in unsupervised coherence modelling represents documents as bipartite graphs of sentences and discourse entities, and then projects these bipartite graphs into one-mode undirected graphs. However, one-mode projections may incur significant loss of the information present in the original bipartite structure. To address this we present three novel graph metrics that compute document coherence on the original bipartite graph of sentences and entities. Evaluation on standard settings shows that: (i) one of our coherence metrics beats the state of the art in terms of coherence accuracy; and (ii) all three of our coherence metrics improve retrieval effectiveness because, as closer analysis reveals, they capture aspects of document quality that go undetected by both keyword-based standard ranking and by spam filtering. This work contributes document coherence metrics that are theoretically principled, parameter-free, and useful to IR.

Connected through hyperedge HE-1 (induced by source paper "weighted bipartite or directed stream graphs for the modeling of temporal networks"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "discovering patterns of interest in ip traffic using cliques in bipartite link streams"
    - "on multiaspect graphs"
    - "a unifying model for representing time varying graphs"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-2 (induced by source paper "dependencies formalising semantic catenae for information retrieval"):
  with members:
    - "fixed versus dynamic co occurrence windows in textrank term weights for information retrieval"
    - "preliminary experiments using subjective logic for the polyrepresentation of information needs"
    - "rhetorical relations for information retrieval"
    - "time series adaptive estimation of vaccination uptake using web search queries"
    - "part of speech based term weighting for information retrieval"
    - "learning to generate compositional color descriptions"
    - "adaptive distributional extensions to dfr ranking"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"

Connected through hyperedge HE-3 (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"
    - "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"
    - "non compositional term dependence for information retrieval"
    - "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)