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: "tweet acts a speech act classifier for twitter"
  Abstract: Speech acts are a way to conceptualize speech as action. This holds true for communication on any platform, including social media platforms such as Twitter. In this paper, we explored speech act recognition on Twitter by treating it as a multi-class classification problem. We created a taxonomy of six speech acts for Twitter and proposed a set of semantic and syntactic features. We trained and tested a logistic regression classifier using a data set of manually labelled tweets. Our method achieved a state-of-the-art performance with an average F1 score of more than $0.70$. We also explored classifiers with three different granularities (Twitter-wide, type-specific and topic-specific) in order to find the right balance between generalization and overfitting for our task.

Connected through hyperedge HE-1 (induced by source paper "target based speech act classification in political campaign text"):
  with members:
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "deep contextualized word representations"
    - "semi supervised sequence modeling with cross view training"
    - "predicting specificity in classroom discussion"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "right wing german hate speech on twitter analysis and automatic detection"):
  with members:
    - "understanding abuse a typology of abusive language detection subtasks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "polarization and fake news early warning of potential misinformation targets"):
  with members:
    - "polarization of the vaccination debate on facebook"
    - "misinformation spreading on facebook"
    - "it s always april fools day on the difficulty of social network misinformation classification via propagation features"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "fake news detection on social media a data mining perspective"
    - "automatically identifying fake news in popular twitter threads"
    - "debunking in a world of tribes"
    - "vews a wikipedia vandal early warning system"

Connected through hyperedge HE-4 (induced by source paper "speech intention understanding in a head final language a disambiguation utilizing intonation dependency"):
  with members:
    - "real time automatic word segmentation for user generated text"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "a structured self attentive sentence embedding"
    - "enriching word vectors with subword information"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "don t get lost in negation an effective negation handled dialogue acts prediction algorithm for twitter customer service conversations"):
  with members:
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "multi task dialog act and sentiment recognition on mastodon"):
  with members:
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"

Connected through hyperedge HE-7 (induced by source paper "how may i help you modeling twitter customer serviceconversations using fine grained dialogue acts"):
  with members:
    - "a semi automatic method for efficient detection of stories on social media"

Connected through hyperedge HE-8 (induced by source paper "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"):
  with members:
    - "deepstance at semeval 2016 task 6 detecting stance in tweets using character and word level cnns"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "skip thought vectors"
    - "text understanding from scratch"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "sequence to sequence learning with 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)