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: "statistical keyword detection in literary corpora"
  Abstract: Understanding the complexity of human language requires an appropriate analysis of the statistical distribution of words in texts. We consider the information retrieval problem of detecting and ranking the relevant words of a text by means of statistical information referring to the spatial use of the words. Shannon's entropy of information is used as a tool for automatic keyword extraction. By using The Origin of Species by Charles Darwin as a representative text sample, we show the performance of our detector and compare it with another proposals in the literature. The random shuffled text receives special attention as a tool for calibrating the ranking indices.

Connected through hyperedge HE-1 (induced by source paper "complex network based supervised keyword extractor"):
  with members:
    - "scake semantic connectivity aware keyword extraction"
    - "unsupervised keyphrase extraction with multipartite graphs"
    - "xgboost a scalable tree boosting system"
    - "supervised topical key phrase extraction of news stories using crowdsourcing light filtering and co reference normalization"
    - "smote synthetic minority over sampling technique"
    - "kea practical automatic keyphrase extraction"
    - "learning to extract keyphrases from text"

Connected through hyperedge HE-2 (induced by source paper "scake semantic connectivity aware keyword extraction"):
  with members:
    - "unsupervised keyphrase extraction with multipartite graphs"
    - "keyword and keyphrase extraction using centrality measures on collocation networks"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "learning algorithms for keyphrase extraction"
    - "kea practical automatic keyphrase extraction"

Connected through hyperedge HE-3 (induced by source paper "a mathematical model for universal semantics"):
  with members:
    - "unsupervised multilingual word embeddings"
    - "a latent variable model approach to pmi based word embeddings"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "topic segmentation via community detection in complex networks"):
  with members:
    - "information theoretical analysis of the statistical dependencies among three variables applications to written language"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "comparing the topological properties of real and artificially generated scientific manuscripts"

Connected through hyperedge HE-5 (induced by source paper "complexity and universality in the long range order of words"):
  with members:
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"
    - "estimating the entropy of binary time series methodology some theory and a simulation study"

Connected through hyperedge HE-6 (induced by source paper "a complex network approach to stylometry"):
  with members:
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "probing the topological properties of complex networks modeling short written texts"
    - "modeling the average shortest path length in growth of word adjacency networks"
    - "a systematic comparison of supervised classifiers"

Connected through hyperedge HE-7 (induced by source paper "probing the topological properties of complex networks modeling short written texts"):
  with members:
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a systematic comparison of supervised classifiers"
    - "discriminating word senses with tourist walks in complex networks"
    - "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts"

Connected through hyperedge HE-8 (induced by source paper "statistical patterns in written language"):
  with members:
    - "estimating the entropy of binary time series methodology some theory and a simulation study"
    - "zipf s law and the creation of musical context"

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)