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: "get out the vote determining support or opposition from congressional floor debate transcripts"
  Abstract: We investigate whether one can determine from the transcripts of U.S. Congressional floor debates whether the speeches represent support of or opposition to proposed legislation. To address this problem, we exploit the fact that these speeches occur as part of a discussion; this allows us to use sources of information regarding relationships between discourse segments, such as whether a given utterance indicates agreement with the opinion expressed by another. We find that the incorporation of such information yields substantial improvements over classifying speeches in isolation.

Connected through hyperedge HE-1 (induced by source paper "stance detection benchmark how robust is your stance detection"):
  with members:
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "stancy stance classification based on consistency cues"
    - "multi task deep neural networks for natural language understanding"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "combating adversarial misspellings with robust word recognition"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "sampling bias in deep active classification an empirical study"

Connected through hyperedge HE-2 (induced by source paper "sentiment analysis of german twitter"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "generating sentiment lexicons for german twitter"
    - "why should i trust you explaining the predictions of any classifier"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "part of speech tagging with bidirectional long short term memory recurrent neural network"
    - "in quest of significance identifying types of twitter sentiment events that predict spikes in sales"

Connected through hyperedge HE-3 (induced by source paper "a survey on computational politics"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "tweeting mps digital engagement between citizens and members of parliament in the uk"
    - "political discussions in homogeneous and cross cutting communication spaces"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "mining the demographics of political sentiment from twitter using learning from label proportions"

Connected through hyperedge HE-4 (induced by source paper "determining relative argument specificity and stance for complex argumentative structures"):
  with members:
    - "domain agnostic real valued specificity prediction"
    - "exploring the role of prior beliefs for argument persuasion"
    - "neural argument generation augmented with externally retrieved evidence"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "predicting specificity in classroom discussion"
    - "debbie the debate bot of the future"
    - "argument mining with structured svms and rnns"
    - "neural network based abstract generation for opinions and arguments"

Connected through hyperedge HE-5 (induced by source paper "evaluating discourse in structured text representations"):
  with members:
    - "inducing grammars with and for neural machine translation"
    - "linguistically informed self attention for semantic role labeling"
    - "discourse structure in machine translation evaluation"
    - "learning structured text representations"
    - "neural discourse structure for text categorization"
    - "learning to compose task specific tree structures"
    - "learning based single document summarization with compression and anaphoricity constraints"
    - "learning to compose words into sentences with reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "sentiment and position taking analysis of parliamentary debates a systematic literature review"):
  with members:
    - "topic specific sentiment analysis can help identify political ideology"
    - "neural discourse structure for text categorization"
    - "understanding state preferences with text as data introducing the un general debate corpus"
    - "stance and sentiment in tweets"
    - "bayesian optimization of text representations"

Connected through hyperedge HE-7 (induced by source paper "orthogonal matching pursuit for text classification"):
  with members:
    - "group sparse cnns for question classification with answer sets"
    - "stabilizing training of generative adversarial networks through regularization"
    - "linguistically regularized lstms for sentiment classification"
    - "distributed representations of words and phrases and their compositionality"
    - "fast approximations to structured sparse coding and applications to object classification"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-8 (induced by source paper "multi view models for political ideology detection of news articles"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "neural variational inference for text processing"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

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)