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: "improving agreement and disagreement identification in online discussions with a socially tuned sentiment lexicon"
  Abstract: We study the problem of agreement and disagreement detection in online discussions. An isotonic Conditional Random Fields (isotonic CRF) based sequential model is proposed to make predictions on sentence- or segment-level. We automatically construct a socially-tuned lexicon that is bootstrapped from existing general-purpose sentiment lexicons to further improve the performance. We evaluate our agreement and disagreement tagging model on two disparate online discussion corpora -- Wikipedia Talk pages and online debates. Our model is shown to outperform the state-of-the-art approaches in both datasets. For example, the isotonic CRF model achieves F1 scores of 0.74 and 0.67 for agreement and disagreement detection, when a linear chain CRF obtains 0.58 and 0.56 for the discussions on Wikipedia Talk pages.

Connected through hyperedge HE-1 (induced by source paper "recognising agreement and disagreement between stances with reason comparing networks"):
  with members:
    - "attention is all you need"
    - "topic independent identification of agreement and disagreement in social media dialogue"
    - "bilateral multi perspective matching for natural language sentences"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "stance detection with bidirectional conditional encoding"
    - "stance and sentiment in tweets"
    - "context dependent translation selection using convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "agree to disagree improving disagreement detection with dual grus"):
  with members:
    - "word affect intensities"
    - "topic independent identification of agreement and disagreement in social media dialogue"
    - "seernet at emoint 2017 tweet emotion intensity estimator"
    - "bilateral multi perspective matching for natural language sentences"
    - "stance and sentiment in tweets"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a large annotated corpus for learning natural language inference"

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)