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: "insight 1 at semeval 2016 task 5 deep learning for multilingual aspect based sentiment analysis"
  Abstract: This paper describes our deep learning-based approach to multilingual aspect-based sentiment analysis as part of SemEval 2016 Task 5. We use a convolutional neural network (CNN) for both aspect extraction and aspect-based sentiment analysis. We cast aspect extraction as a multi-label classification problem, outputting probabilities over aspects parameterized by a threshold. To determine the sentiment towards an aspect, we concatenate an aspect vector with every word embedding and apply a convolution over it. Our constrained system (unconstrained for English) achieves competitive results across all languages and domains, placing first or second in 5 and 7 out of 11 language-domain pairs for aspect category detection (slot 1) and sentiment polarity (slot 3) respectively, thereby demonstrating the viability of a deep learning-based approach for multilingual aspect-based sentiment analysis.

Connected through hyperedge HE-1 (induced by source paper "a novel aspect guided deep transition model for aspect based sentiment analysis"):
  with members:
    - "context aware embedding for targeted aspect based sentiment analysis"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "open domain targeted sentiment analysis via span based extraction and classification"
    - "an interactive multi task learning network for end to end aspect based sentiment analysis"
    - "neural aspect and opinion term extraction with mined rules as weak supervision"
    - "multi task learning for multi modal emotion recognition and sentiment analysis"
    - "doer dual cross shared rnn for aspect term polarity co extraction"
    - "latent variable sentiment grammar"

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"
    - "semi supervised classification with graph convolutional networks"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-3 (induced by source paper "open domain event extraction and embedding for natural gas market prediction"):
  with members:
    - "forecasting economics and financial time series arima vs lstm"
    - "cevo comprehensive event ontology enhancing cognitive annotation"
    - "temporal attention augmented bilinear network for financial time series data analysis"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "leverage financial news to predict stock price movements using word embeddings and deep neural networks"
    - "modelling visualising and summarising documents with a single convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "aspect based sentiment analysis with gated convolutional networks"):
  with members:
    - "a hierarchical model of reviews for aspect based sentiment analysis"
    - "language modeling with gated convolutional networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-5 (induced by source paper "representation stability as a regularizer for improved text analytics transfer learning"):
  with members:
    - "progressive neural networks"
    - "harnessing deep neural networks with logic rules"
    - "learning without forgetting"
    - "net2net accelerating learning via knowledge transfer"
    - "improved semantic representations from tree structured long short term memory networks"
    - "ask me anything dynamic memory networks for natural language processing"
    - "fitnets hints for thin deep nets"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-6 (induced by source paper "a hierarchical model of reviews for aspect based sentiment analysis"):
  with members:
    - "harnessing deep neural networks with logic rules"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "effective lstms for target dependent sentiment classification"
    - "adam a method for stochastic optimization"
    - "extraction of salient sentences from labelled documents"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"

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)