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: "beyond word frequency bursts lulls and scaling in the temporal distributions of words"
  Abstract: Background #R##N#Zipf's discovery that word frequency distributions obey a power law established parallels between biological and physical processes, and language, laying the groundwork for a complex systems perspective on human communication. More recent research has also identified scaling regularities in the dynamics underlying the successive occurrences of events, suggesting the possibility of similar findings for language as well.#R##N#Methodology/Principal Findings #R##N#By considering frequent words in USENET discussion groups and in disparate databases where the language has different levels of formality, here we show that the distributions of distances between successive occurrences of the same word display bursty deviations from a Poisson process and are well characterized by a stretched exponential (Weibull) scaling. The extent of this deviation depends strongly on semantic type – a measure of the logicality of each word – and less strongly on frequency. We develop a generative model of this behavior that fully determines the dynamics of word usage.#R##N#Conclusions/Significance #R##N#Recurrence patterns of words are well described by a stretched exponential distribution of recurrence times, an empirical scaling that cannot be anticipated from Zipf's law. Because the use of words provides a uniquely precise and powerful lens on human thought and activity, our findings also have implications for other overt manifestations of collective human dynamics.

Connected through hyperedge HE-1 (induced by source paper "computational human dynamics"):
  with members:
    - "joint embedding of structure and features via graph convolutional networks"
    - "location occupation and semantics based socioeconomic status inference on twitter"
    - "the temporal event graph"
    - "link transmission centrality in large scale social networks"
    - "socioeconomic dependencies of linguistic patterns in twitter a multivariate analysis"
    - "graph embedding techniques applications and performance a survey"
    - "correlations and dynamics of consumption patterns in social economic networks"
    - "stream graphs and link streams for the modeling of interactions over time"

Connected through hyperedge HE-2 (induced by source paper "evaluating computational language models with scaling properties of natural language"):
  with members:
    - "taylor s law for human linguistic sequences"
    - "an analysis of neural language modeling at multiple scales"
    - "cot cooperative training for generative modeling of discrete data"
    - "texygen a benchmarking platform for text generation models"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "do neural nets learn statistical laws behind natural language"
    - "adversarial generation of natural language"

Connected through hyperedge HE-3 (induced by source paper "a new evaluation framework for topic modeling algorithms based on synthetic corpora"):
  with members:
    - "provable algorithms for inference in topic models"
    - "integrating document clustering and topic modeling"
    - "a practical algorithm for topic modeling with provable guarantees"

Connected through hyperedge HE-4 (induced by source paper "assessing language models with scaling properties"):
  with members:
    - "taylor s law for human linguistic sequences"
    - "lstm a search space odyssey"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "do neural nets learn statistical laws behind natural language"
    - "regularizing and optimizing lstm language models"
    - "improving neural language models with a continuous cache"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-5 (induced by source paper "representation of texts as complex networks a mesoscopic approach"):
  with members:
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "using complex networks for text classification discriminating informative and imaginative documents"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "modeling the average shortest path length in growth of word adjacency networks"
    - "authorship attribution through function word adjacency networks"
    - "word length entropies and correlations of natural language written texts"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "robustness of sentence length measures in written texts"):
  with members:
    - "in narrative texts punctuation marks obey the same statistics as words"
    - "compression and the origins of zipf s law for word frequencies"
    - "text mixing shapes the anatomy of rank frequency distributions"
    - "modeling the average shortest path length in growth of word adjacency networks"
    - "punctuation effects in english and esperanto texts"

Connected through hyperedge HE-7 (induced by source paper "taylor s law for human linguistic sequences"):
  with members:
    - "assessing language models with scaling properties"

Connected through hyperedge HE-8 (induced by source paper "do neural nets learn statistical laws behind natural language"):
  with members:
    - "n gram language modeling using recurrent neural network estimation"
    - "pointing the unknown words"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "samplernn an unconditional end to end neural audio generation model"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "neural machine translation in linear time"
    - "wavenet a generative model for raw audio"
    - "google s neural machine translation system bridging the gap between human and machine translation"

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)