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: "ensemble of generative and discriminative techniques for sentiment analysis of movie reviews"
  Abstract: Sentiment analysis is a common task in natural language processing that aims to detect polarity of a text document (typically a consumer review). In the simplest settings, we discriminate only between positive and negative sentiment, turning the task into a standard binary classification problem. We compare several ma- chine learning approaches to this problem, and combine them to achieve the best possible results. We show how to use for this task the standard generative lan- guage models, which are slightly complementary to the state of the art techniques. We achieve strong results on a well-known dataset of IMDB movie reviews. Our results are easily reproducible, as we publish also the code needed to repeat the experiments. This should simplify further advance of the state of the art, as other researchers can combine their techniques with ours with little effort.

Connected through hyperedge HE-1 (induced by source paper "derivative manipulation for general example weighting"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "deep learning is robust to massive label noise"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "deep metric learning by online soft mining and class aware attention"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"

Connected through hyperedge HE-2 (induced by source paper "sentiment analysis using averaged weighted word vector features"):
  with members:
    - "identifying restaurant features via sentiment analysis on yelp reviews"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"
    - "adadelta an adaptive learning rate method"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-3 (induced by source paper "topic spotting using hierarchical networks with self attention"):
  with members:
    - "attention is all you need"
    - "transition based dependency parsing with stack long short term memory"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "learning to generate reviews and discovering sentiment"):
  with members:
    - "toward controlled generation of text"
    - "deep learning with dynamic computation graphs"
    - "multiplicative lstm for sequence modelling"
    - "neural semantic encoders"
    - "learning distributed representations of sentences from unlabelled data"
    - "understanding neural networks through representation erasure"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "towards language agnostic universal representations"):
  with members:
    - "unsupervised multilingual word embeddings"
    - "deep contextualized word representations"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "learning to generate reviews and discovering sentiment"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "learned in translation contextualized word vectors"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "using the tsetlin machine to learn human interpretable rules for high accuracy text categorization with medical applications"):
  with members:
    - "the tsetlin machine a game theoretic bandit driven approach to optimal pattern recognition with propositional logic"
    - "universal language model fine tuning for text classification"
    - "learning to generate reviews and discovering sentiment"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "starspace embed all the things"
    - "very deep convolutional networks for text classification"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "deep learning in neural networks"

Connected through hyperedge HE-7 (induced by source paper "exploiting deep learning for persian sentiment analysis"):
  with members:
    - "a hybrid convolutional variational autoencoder for text generation"
    - "fasttext zip compressing text classification models"
    - "semi supervised sequence learning"
    - "semisupervised autoencoder for sentiment analysis"
    - "convolutional neural networks for sentence classification"
    - "an autoencoder approach to learning bilingual word representations"
    - "cutting recursive autoencoder trees"
    - "marginalized denoising autoencoders for domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "multi view ensemble classification for clinically actionable genetic mutations"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "xgboost a scalable tree boosting system"
    - "character level convolutional networks for text classification"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "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)