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: "modeling online reviews with multi grain topic models"
  Abstract: In this paper we present a novel framework for extracting the ratable aspects of objects from online user reviews. Extracting such aspects is an important challenge in automatically mining product opinions from the web and in generating opinion-based summaries of user reviews. Our models are based on extensions to standard topic modeling methods such as LDA and PLSA to induce multi-grain topics. We argue that multi-grain models are more appropriate for our task since standard models tend to produce topics that correspond to global properties of objects (e.g., the brand of a product type) rather than the aspects of an object that tend to be rated by a user. The models we present not only extract ratable aspects, but also cluster them into coherent topics, e.g., `waitress' and `bartender' are part of the same topic `staff' for restaurants. This differentiates it from much of the previous work which extracts aspects through term frequency analysis with minimal clustering. We evaluate the multi-grain models both qualitatively and quantitatively to show that they improve significantly upon standard topic models.

Connected through hyperedge HE-1 (induced by source paper "vsec lda boosting topic modeling with embedded vocabulary selection"):
  with members:
    - "the dynamic embedded topic model"
    - "probabilistic latent semantic analysis"
    - "expectation propagation for approximate bayesian inference"
    - "applying discrete pca in data analysis"

Connected through hyperedge HE-2 (induced by source paper "cross domain aspect category transfer and detection via traceable heterogeneous graph representation learning"):
  with members:
    - "mathematics content understanding for cyberlearning via formula evolution map"
    - "cross language citation recommendation via hierarchical representation learning on heterogeneous graph"
    - "attention is all you need"
    - "recent trends in deep learning based natural language processing"
    - "aspect level sentiment classification with deep memory network"
    - "node2vec scalable feature learning for networks"
    - "insight 1 at semeval 2016 task 5 deep learning for multilingual aspect based sentiment analysis"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "unsupervised multi document opinion summarization as copycat review generation"):
  with members:
    - "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"
    - "generating wikipedia by summarizing long sequences"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "meansum a neural model for unsupervised multi document abstractive summarization"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

Connected through hyperedge HE-4 (induced by source paper "a probabilistic framework for learning domain specific hierarchical word embeddings"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "neural collaborative filtering"
    - "learned in translation contextualized word vectors"
    - "a mixture model for learning multi sense word embeddings"
    - "structured embedding models for grouped data"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-5 (induced by source paper "aspect specific opinion expression extraction using attention based lstm crf network"):
  with members:
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "aspect level sentiment classification with deep memory network"
    - "unsupervised word and dependency path embeddings for aspect term extraction"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "effective lstms for target dependent sentiment classification"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "leveraging structural and semantic correspondence for attribute oriented aspect sentiment discovery"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "generating sentences from disentangled syntactic and semantic spaces"
    - "disentangled representation learning for non parallel text style transfer"
    - "improving topic models with latent feature word representations"
    - "learning disentangled representations of texts with application to biomedical abstracts"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "attention is all you need"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"):
  with members:
    - "multiple instance learning networks for fine grained sentiment analysis"
    - "best worst scaling more reliable than rating scales a case study on sentiment intensity annotation"
    - "graph based neural multi document summarization"
    - "neural network based abstract generation for opinions and arguments"
    - "unsupervised word and dependency path embeddings for aspect term extraction"
    - "better document level sentiment analysis from rst discourse parsing"
    - "image based recommendations on styles and substitutes"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "transformation networks for target oriented sentiment classification"):
  with members:
    - "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
    - "identity mappings in deep residual networks"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "training very deep networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "representation learning a review and new perspectives"

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)