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: "between conjecture and memento shaping a collective emotional perception of the future"
  Abstract: Large scale surveys of public mood are costly and often impractical to perform. However, the web is awash with material indicative of public mood such as blogs, emails, and web queries. Inexpensive content analysis on such extensive corpora can be used to assess public mood fluctuations. The work presented here is concerned with the analysis of the public mood towards the future. Using an extension of the Profile of Mood States questionnaire, we have extracted mood indicators from 10,741 emails submitted in 2006 to futureme.org, a web service that allows its users to send themselves emails to be delivered at a later date. Our results indicate long-term optimism toward the future, but medium-term apprehension and confusion.

Connected through hyperedge HE-1 (induced by source paper "discovering basic emotion sets via semantic clustering on a twitter corpus"):
  with members:
    - "negative emotions boost user activity at bbc forum"
    - "twitter mood predicts the stock market"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-2 (induced by source paper "truthy mapping the spread of astroturf in microblog streams"):
  with members:
    - "how does market concern derived from the internet affect oil prices"
    - "twitter mood predicts the stock market"
    - "inferring networks of diffusion and influence"
    - "influence and passivity in social media"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-3 (induced by source paper "twitter mood predicts the stock market"):
  with members:
    - "how does market concern derived from the internet affect oil prices"
    - "happiness is assortative in online social networks"
    - "measuring the happiness of large scale written expression songs blogs and presidents"

Connected through hyperedge HE-4 (induced by source paper "modeling public mood and emotion twitter sentiment and socio economic phenomena"):
  with members:
    - "measuring the happiness of large scale written expression songs blogs and presidents"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

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)