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: "modeling rich contexts for sentiment classification with lstm"
  Abstract: Sentiment analysis on social media data such as tweets and weibo has become a very important and challenging task. Due to the intrinsic properties of such data, tweets are short, noisy, and of divergent topics, and sentiment classification on these data requires to modeling various contexts such as the retweet/reply history of a tweet, and the social context about authors and relationships. While few prior study has approached the issue of modeling contexts in tweet, this paper proposes to use a hierarchical LSTM to model rich contexts in tweet, particularly long-range context. Experimental results show that contexts can help us to perform sentiment classification remarkably better.

Connected through hyperedge HE-1 (induced by source paper "tweets can tell activity recognition using hybrid long short term memory model"):
  with members:
    - "lstm a search space odyssey"
    - "attention is all you need"
    - "tweet2vec character based distributed representations for social media"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "learning natural language inference using bidirectional lstm model and inner attention"
    - "a c lstm neural network for text classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "comparative studies of detecting abusive language on twitter"):
  with members:
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "mean birds detecting aggression and bullying on twitter"
    - "deep learning for hate speech detection in tweets"
    - "learning to rank question answer pairs using hierarchical recurrent encoder with latent topic clustering"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "parallel long short term memory for multi stream classification"):
  with members:
    - "show and tell a neural image caption generator"
    - "theano new features and speed improvements"

Connected through hyperedge HE-4 (induced by source paper "utcnn a deep learning model of stance classificationon on social media text"):
  with members:
    - "hinge loss markov random fields and probabilistic soft logic"
    - "distributed representations of sentences and documents"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "a convolutional neural network for modelling sentences"
    - "get out the vote determining support or opposition from congressional floor debate transcripts"

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)