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: "towards better text understanding and retrieval through kernel entity salience modeling"
  Abstract: This paper presents a Kernel Entity Salience Model (KESM) that improves text understanding and retrieval by better estimating entity salience (importance) in documents. KESM represents entities by knowledge enriched distributed representations, models the interactions between entities and words by kernels, and combines the kernel scores to estimate entity salience. The whole model is learned end-to-end using entity salience labels. The salience model also improves ad hoc search accuracy, providing effective ranking features by modeling the salience of query entities in candidate documents. Our experiments on two entity salience corpora and two TREC ad hoc search datasets demonstrate the effectiveness of KESM over frequency-based and feature-based methods. We also provide examples showing how KESM conveys its text understanding ability learned from entity salience to search.

Connected through hyperedge HE-1 (induced by source paper "selective weak supervision for neural information retrieval"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "document expansion by query prediction"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "understanding the behaviors of bert in ranking"
    - "cedr contextualized embeddings for document ranking"

Connected through hyperedge HE-2 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "graph representation learning via hard and channel wise attention networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hypergraph neural networks"
    - "graph convolutional networks for text classification"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "deepinf social influence prediction with deep learning"
    - "learning structural node embeddings via diffusion wavelets"

Connected through hyperedge HE-3 (induced by source paper "open domain web keyphrase extraction beyond language modeling"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "deep contextualized word representations"
    - "automatic event salience identification"
    - "semi supervised learning for neural keyphrase generation"
    - "generating diverse numbers of diverse keyphrases"
    - "title guided encoding for keyphrase generation"
    - "attention is all you need"
    - "deep keyphrase generation"

Connected through hyperedge HE-4 (induced by source paper "automatic event salience identification"):
  with members:
    - "swat a system for detecting salient wikipedia entities in texts"
    - "implicit argument prediction with event knowledge"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "using sentence level lstm language models for script inference"
    - "adam a method for stochastic optimization"
    - "probabilistic frame induction"
    - "distributed representations of words and phrases and their compositionality"
    - "tagme on the fly annotation of short text fragments by wikipedia entities"

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)