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: "word statistics in blogs and rss feeds towards empirical universal evidence"
  Abstract: We focus on the statistics of word occurrences and of the waiting times between such occurrences in Blogs. Due to the heterogeneity of words' frequencies, the empirical analysis is performed by studying classes of "frequently-equivalent" words, i.e. by grouping words depending on their frequencies. Two limiting cases are considered: the dilute limit, i.e. for those words that are used less than once a day, and the dense limit for frequent words. In both cases, extreme events occur more frequently than expected from the Poisson hypothesis. These deviations from Poisson statistics reveal non-trivial time correlations between events that are associated with bursts of activities. The distribution of waiting times is shown to behave like a stretched exponential and to have the same shape for different sets of words sharing a common frequency, thereby revealing universal features.

Connected through hyperedge HE-1 (induced by source paper "local variation of hashtag spike trains and popularity in twitter"):
  with members:
    - "the bursty dynamics of the twitter information network"
    - "attention competition with advertisement"
    - "word storms multiples of word clouds for visual comparison of documents"
    - "virality prediction and community structure in social networks"

Connected through hyperedge HE-2 (induced by source paper "word length entropies and correlations of natural language written texts"):
  with members:
    - "entropy analysis of word length series of natural language texts effects of text language and genre"

Connected through hyperedge HE-3 (induced by source paper "a practical approach to language complexity a wikipedia case study"):
  with members:
    - "niche as a determinant of word fate in online groups"
    - "for the sake of simplicity unsupervised extraction of lexical simplifications from wikipedia"
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"
    - "wikipedia based semantic interpretation for natural language processing"
    - "mining meaning from wikipedia"

Connected through hyperedge HE-4 (induced by source paper "punctuation effects in english and esperanto texts"):
  with members:
    - "approaching the linguistic complexity"

Connected through hyperedge HE-5 (induced by source paper "beyond word frequency bursts lulls and scaling in the temporal distributions of words"):
  with members:
    - "statistical keyword detection in literary corpora"
    - "novelty and collective attention"

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.IT(Information Theory)