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: "deepstance at semeval 2016 task 6 detecting stance in tweets using character and word level cnns"
  Abstract: This paper describes our approach for the Detecting Stance in Tweets task (SemEval-2016 Task 6). We utilized recent advances in short text categorization using deep learning to create word-level and character-level models. The choice between word-level and character-level models in each particular case was informed through validation performance. Our final system is a combination of classifiers using word-level or character-level models. We also employed novel data augmentation techniques to expand and diversify our training dataset, thus making our system more robust. Our system achieved a macro-average precision, recall and F1-scores of 0.67, 0.61 and 0.635 respectively.

Connected through hyperedge HE-1 (induced by source paper "neural language model based training data augmentation for weakly supervised early rumor detection"):
  with members:
    - "all in one multi task learning for rumour verification"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "deep contextualized word representations"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "a longitudinal assessment of the persistence of twitter datasets"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "learning reporting dynamics during breaking news for rumour detection in social media"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "topical stance detection for twitter a two phase lstm model using attention"):
  with members:
    - "stance detection with bidirectional conditional encoding"
    - "mitre at semeval 2016 task 6 transfer learning for stance detection"
    - "stance and sentiment in tweets"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "globally normalized reader"):
  with members:
    - "learning recurrent span representations for extractive question answering"
    - "neural question generation from text a preliminary study"
    - "making neural qa as simple as possible but not simpler"
    - "newsqa a machine comprehension dataset"
    - "sequence to sequence learning as beam search optimization"
    - "a parallel hierarchical model for machine comprehension on sparse data"
    - "bidirectional attention flow for machine comprehension"
    - "distant supervision for relation extraction beyond the sentence boundary"

Connected through hyperedge HE-4 (induced by source paper "friends and enemies of clinton and trump using context for detecting stance in political tweets"):
  with members:
    - "a readability analysis of campaign speeches from the 2016 us presidential campaign"
    - "nlds ucsc at semeval 2016 task 6 a semi supervised approach to detecting stance in tweets"
    - "mitre at semeval 2016 task 6 transfer learning for stance detection"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "crowdsourcing a word emotion association lexicon"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"

Connected through hyperedge HE-5 (induced by source paper "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"):
  with members:
    - "tweet acts a speech act classifier for twitter"
    - "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)