human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "comparing the topological properties of real and artificially generated scientific manuscripts"):
  Abstract: Recent years have witnessed the increase of competition in science. While promoting the quality of research in many cases, an intense competition among scientists can also trigger unethical scientific behaviors. To increase the total number of published papers, some authors even resort to software tools that are able to produce grammatical, but meaningless scientific manuscripts. Because automatically generated papers can be misunderstood as real papers, it becomes of paramount importance to develop means to identify these scientific frauds. In this paper, I devise a methodology to distinguish real manuscripts from those generated with SCIGen, an automatic paper generator. Upon modeling texts as complex networks (CN), it was possible to discriminate real from fake papers with at least 89 % of accuracy. A systematic analysis of features relevance revealed that the accessibility and betweenness were useful in particular cases, even though the relevance depended upon the dataset. The successful application of the methods described here show, as a proof of principle, that network features can be used to identify scientific gibberish papers. In addition, the CN-based approach can be combined in a straightforward fashion with traditional statistical language processing methods to improve the performance in identifying artificially generated papers.

Includes members:
  Member "patterns of text reuse in a scientific corpus":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wikipedia text reuse within and without")
  Member "probing the topological properties of complex networks modeling short written texts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")
  Member "a systematic comparison of supervised classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "paragraph based representation of texts a complex networks approach")
  Member "measuring structural distances between texts":
    also connected through other hyperedges:
      (none)
  Member "discriminating word senses with tourist walks in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "word sense disambiguation a complex network approach")
  Member "scientific impact evaluation and the effect of self citations mitigating the bias by discounting h index":
    also connected through other hyperedges:
      (none)
  Member "a practical approach to language complexity a wikipedia case study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a standardized project gutenberg corpus for statistical analysis of natural language and quantitative linguistics")
  Member "the similarity metric":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "max margin class imbalanced learning with gaussian affinity")

Question: Please tell me which class the source paper 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)