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: "cold start aware user and product attention for sentiment classification"
  Abstract: The use of user/product information in sentiment analysis is important, especially for cold-start users/products, whose number of reviews are very limited. However, current models do not deal with the cold-start problem which is typical in review websites. In this paper, we present Hybrid Contextualized Sentiment Classifier (HCSC), which contains two modules: (1) a fast word encoder that returns word vectors embedded with short and long range dependency features; and (2) Cold-Start Aware Attention (CSAA), an attention mechanism that considers the existence of cold-start problem when attentively pooling the encoded word vectors. HCSC introduces shared vectors that are constructed from similar users/products, and are used when the original distinct vectors do not have sufficient information (i.e. cold-start). This is decided by a frequency-guided selective gate vector. Our experiments show that in terms of RMSE, HCSC performs significantly better when compared with on famous datasets, despite having less complexity, and thus can be trained much faster. More importantly, our model performs significantly better than previous models when the training data is sparse and has cold-start problems.

Connected through hyperedge HE-1 (induced by source paper "rethinking attribute representation and injection for sentiment classification"):
  with members:
    - "categorical metadata representation for customized text classification"
    - "dual memory network model for biased product review classification"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "translations as additional contexts for sentence classification"
    - "improving review representations with user attention and product attention for sentiment classification"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "extreme adaptation for personalized neural machine translation"
    - "controlling linguistic style aspects in neural language generation"

Connected through hyperedge HE-2 (induced by source paper "harnessing gans for addition of new classes in vsr"):
  with members:
    - "generative adversarial networks an overview"
    - "modality attention for end to end audio visual speech recognition"
    - "low shot learning from imaginary data"
    - "harnessing ai for speech reconstruction using multi view silent video feed"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "lip2audspec speech reconstruction from silent lip movements video"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "end to end multi view lipreading"

Connected through hyperedge HE-3 (induced by source paper "categorical metadata representation for customized text classification"):
  with members:
    - "dual memory network model for biased product review classification"
    - "translations as additional contexts for sentence classification"
    - "deep contextualized word representations"
    - "automatic academic paper rating based on modularized hierarchical convolutional neural network"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "effective approaches to attention based neural machine translation"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"

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)