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: "benchmarking sentiment analysis methods for large scale texts a case for using continuum scored words and word shift graphs"
  Abstract: The emergence and global adoption of social media has rendered possible the real-time estimation of population-scale sentiment, bearing profound implications for our understanding of human behavior. Given the growing assortment of sentiment measuring instruments, comparisons between them are evidently required. Here, we perform detailed tests of 6 dictionary-based methods applied to 4 different corpora, and briefly examine a further 20 methods. We show that a dictionary-based method will only perform both reliably and meaningfully if (1) the dictionary covers a sufficiently large enough portion of a given text's lexicon when weighted by word usage frequency; and (2) words are scored on a continuous scale.

Connected through hyperedge HE-1 (induced by source paper "mining social media data for biomedical signals and health related behavior"):
  with members:
    - "singular value decomposition and neural networks"
    - "anatomy of an online misinformation network"
    - "human sexual cycles are driven by culture and match collective moods"
    - "limited individual attention and online virality of low quality information"
    - "quantified self meets social media sharing of weight updates on twitter"
    - "the rise of social bots"
    - "learning deep structure preserving image text embeddings"
    - "insights from machine learned diet success prediction"

Connected through hyperedge HE-2 (induced by source paper "benchmarking top k keyword and top k document processing with t2k2 and t2k2d2"):
  with members:
    - "a scalable document based architecture for text analysis"
    - "event detection tracking and visualization in twitter a mention anomaly based approach"
    - "bigdatabench a big data benchmark suite from internet services"
    - "primeball a parallel processing framework benchmark for big data applications in the cloud"

Connected through hyperedge HE-3 (induced by source paper "towards a science of human stories using sentiment analysis and emotional arcs to understand the building blocks of complex social systems"):
  with members:
    - "ultradense word embeddings by orthogonal transformation"
    - "boundary based mwe segmentation with text partitioning"
    - "probabilistic frame induction"
    - "learning frames from text with an unsupervised latent variable model"
    - "crowdsourcing a word emotion association lexicon"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-4 (induced by source paper "t 2 k 2 the twitter top k keywords benchmark"):
  with members:
    - "a scalable document based architecture for text analysis"
    - "event detection tracking and visualization in twitter a mention anomaly based approach"
    - "lshtc a benchmark for large scale text classification"
    - "data processing benchmarks"
    - "primeball a parallel processing framework benchmark for big data applications in the cloud"

Connected through hyperedge HE-5 (induced by source paper "the emotional arcs of stories are dominated by six basic shapes"):
  with members:
    - "narrative smoothing dynamic conversational network for the analysis of tv series plots"

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)