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: "w2vlda almost unsupervised system for aspect based sentiment analysis"
  Abstract: With the increase of online customer opinions in specialised websites and social networks, the necessity of automatic systems to help to organise and classify customer reviews by domain-specific aspect/categories and sentiment polarity is more important than ever. Supervised approaches to Aspect Based Sentiment Analysis obtain good results for the domain/language their are trained on, but having manually labelled data for training supervised systems for all domains and languages are usually very costly and time consuming. In this work we describe W2VLDA, an almost unsupervised system based on topic modelling, that combined with some other unsupervised methods and a minimal configuration, performs aspect/category classifiation, aspect-terms/opinion-words separation and sentiment polarity classification for any given domain and language. We evaluate the performance of the aspect and sentiment classification in the multilingual SemEval 2016 task 5 (ABSA) dataset. We show competitive results for several languages (English, Spanish, French and Dutch) and domains (hotels, restaurants, electronic-devices).

Connected through hyperedge HE-1 (induced by source paper "leveraging deep graph based text representation for sentiment polarity applications"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "memory enriched big bang big crunch optimization algorithm for data clustering"
    - "fasttext zip compressing text classification models"
    - "node2vec scalable feature learning for networks"
    - "relevant based structure learning for feature selection"
    - "deep learning in neural networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "deep sentiment analysis using a graph based text representation"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "memory enriched big bang big crunch optimization algorithm for data clustering"
    - "fasttext zip compressing text classification models"
    - "node2vec scalable feature learning for networks"
    - "relevant based structure learning for feature selection"
    - "deep learning in neural networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "implicit dimension identification in user generated text with lstm networks"):
  with members:
    - "recurrent ladder networks"
    - "deep learning for hate speech detection in tweets"
    - "fake news detection on social media a data mining perspective"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "pointer sentinel mixture models"
    - "morphological inflection generation with hard monotonic attention"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "choosing the right word using bidirectional lstm tagger for writing support systems"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "lstm a search space odyssey"
    - "fake news detection on social media a data mining perspective"
    - "paraphrase generation with deep reinforcement learning"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "exploring the limits of language modeling"
    - "pointer sentinel mixture models"
    - "a unified tagging solution bidirectional lstm recurrent neural network with word embedding"

Connected through hyperedge HE-5 (induced by source paper "a model for predicting price polarity of real estate properties using information of real estate market websites"):
  with members:
    - "vision based real estate price estimation"
    - "xgboost a scalable tree boosting system"
    - "distributed representations of sentences and documents"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "unsupervised aspect term extraction with b lstm crf using automatically labelled datasets"):
  with members:
    - "neuroner an easy to use program for named entity recognition based on neural networks"
    - "automated phrase mining from massive text corpora"
    - "unsupervised word and dependency path embeddings for aspect term extraction"
    - "multi task cross lingual sequence tagging from scratch"
    - "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)