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: "linguistically regularized lstms for sentiment classification"
  Abstract: Sentiment understanding has been a long-term goal of AI in the past decades. This paper deals with sentence-level sentiment classification. Though a variety of neural network models have been proposed very recently, however, previous models either depend on expensive phrase-level annotation, whose performance drops substantially when trained with only sentence-level annotation; or do not fully employ linguistic resources (e.g., sentiment lexicons, negation words, intensity words), thus not being able to produce linguistically coherent representations. In this paper, we propose simple models trained with sentence-level annotation, but also attempt to generating linguistically coherent representations by employing regularizers that model the linguistic role of sentiment lexicons, negation words, and intensity words. Results show that our models are effective to capture the sentiment shifting effect of sentiment, negation, and intensity words, while still obtain competitive results without sacrificing the models' simplicity.

Connected through hyperedge HE-1 (induced by source paper "thick net parallel network structure for sequential modeling"):
  with members:
    - "going wider recurrent neural network with parallel cells"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "recurrent dropout without memory loss"
    - "recurrent highway networks"
    - "improving neural language models with a continuous cache"

Connected through hyperedge HE-2 (induced by source paper "off policy evaluation in partially observable environments"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "batch policy learning under constraints"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "evaluating reinforcement learning algorithms in observational health settings"
    - "deconfounding reinforcement learning in observational settings"
    - "contextual bandits with latent confounders an nmf approach"

Connected through hyperedge HE-3 (induced by source paper "sentence state lstm for text representation"):
  with members:
    - "attention is all you need"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "dynamic routing between capsules"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "densely connected bidirectional lstm with applications to sentence classification"):
  with members:
    - "improved neural relation detection for knowledge base question answering"
    - "multichannel variable size convolution for sentence classification"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "improved semantic representations from tree structured long short term memory networks"
    - "highway long short term memory rnns for distant speech recognition"

Connected through hyperedge HE-5 (induced by source paper "investigating capsule networks with dynamic routing for text classification"):
  with members:
    - "fast dynamic routing based on weighted kernel density estimation"
    - "dynamic routing between capsules"
    - "improved semantic representations from tree structured long short term memory networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-6 (induced by source paper "dual memory network model for biased product review classification"):
  with members:
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "leveraging cognitive features for sentiment analysis"
    - "aspect level sentiment classification with deep memory network"
    - "cached long short term memory neural networks for document level sentiment classification"
    - "end to end memory networks"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-7 (induced by source paper "orthogonal matching pursuit for text classification"):
  with members:
    - "group sparse cnns for question classification with answer sets"
    - "stabilizing training of generative adversarial networks through regularization"
    - "distributed representations of words and phrases and their compositionality"
    - "fast approximations to structured sparse coding and applications to object classification"
    - "get out the vote determining support or opposition from congressional floor debate transcripts"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-8 (induced by source paper "estimation of inter sentiment correlations employing deep neural network models"):
  with members:
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "adversarial multi task learning for text classification"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"

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.CL(Computation and Language)