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: "fostering user engagement rhetorical devices for applause generation learnt from ted talks"
  Abstract: One problem that every presenter faces when delivering a public discourse is how to hold the listeners' attentions or to keep them involved. Therefore, many studies in conversation analysis work on this issue and suggest qualitatively con-structions that can effectively lead to audience's applause. To investigate these proposals quantitatively, in this study we an-alyze the transcripts of 2,135 TED Talks, with a particular fo-cus on the rhetorical devices that are used by the presenters for applause elicitation. Through conducting regression anal-ysis, we identify and interpret 24 rhetorical devices as triggers of audience applauding. We further build models that can rec-ognize applause-evoking sentences and conclude this work with potential implications.

Connected through hyperedge HE-1 (induced by source paper "predicting ted talk ratings from language and prosody"):
  with members:
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "convolutional neural network for humor recognition"
    - "regularizing and optimizing lstm language models"
    - "using argument based features to predict and analyse review helpfulness"
    - "globally normalized transition based neural networks"
    - "automatic text scoring using neural networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "a causality guided prediction of the ted talk ratings from the speech transcripts using neural networks"):
  with members:
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "convolutional neural network for humor recognition"
    - "regularizing and optimizing lstm language models"
    - "using argument based features to predict and analyse review helpfulness"
    - "globally normalized transition based neural networks"
    - "automatic text scoring using neural networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"

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)