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: "quantifying the cognitive extent of science"
  Abstract: While the modern science is characterized by an exponential growth in scientific literature, the increase in publication volume clearly does not reflect the expansion of the cognitive boundaries of science. Nevertheless, most of the metrics for assessing the vitality of science or for making funding and policy decisions are based on productivity. Similarly, the increasing level of knowledge production by large science teams, whose results often enjoy greater visibility, does not necessarily mean that "big science" leads to cognitive expansion. Here we present a novel, big-data method to quantify the extents of cognitive domains of different bodies of scientific literature independently from publication volume, and apply it to 20 million articles published over 60-130 years in physics, astronomy, and biomedicine. The method is based on the lexical diversity of titles of fixed quotas of research articles. Owing to large size of quotas, the method overcomes the inherent stochasticity of article titles to achieve <1% precision. We show that the periods of cognitive growth do not necessarily coincide with the trends in publication volume. Furthermore, we show that the articles produced by larger teams cover significantly smaller cognitive territory than (the same quota of) articles from smaller teams. Our findings provide a new perspective on the role of small teams and individual researchers in expanding the cognitive boundaries of science. The proposed method of quantifying the extent of the cognitive territory can also be applied to study many other aspects of "science of science."

Connected through hyperedge HE-1 (induced by source paper "predicting research trends with semantic and neural networks with an application in quantum physics"):
  with members:
    - "the emergent integrated network structure of scientific research"
    - "a comprehensive survey on graph neural networks"
    - "the landscape of neuroimage ing research"
    - "attention is all you need"
    - "gated graph sequence neural networks"
    - "learning convolutional neural networks for graphs"
    - "inheritance patterns in citation networks reveal scientific memes"
    - "coauthorship and citation patterns in the physical review"

Connected through hyperedge HE-2 (induced by source paper "citations indicators of quality the impact fallacy"):
  with members:
    - "introducing citedreferencesexplorer crexplorer a program for reference publication year spectroscopy with cited references standardization"
    - "identification of long term concept symbols among citations can documents be clustered in terms of common intellectual histories"
    - "interpreting altmetrics viewing acts on social media through the lens of citation and social theories"
    - "the operationalization of fields as wos subject categories wcs in evaluative bibliometrics the cases of library and information science and science technology studies"
    - "the role of handbooks in knowledge creation and diffusion a case of science and technology studies"
    - "citnetexplorer a new software tool for analyzing and visualizing citation networks"
    - "detecting the historical roots of research fields by reference publication year spectroscopy rpys"
    - "referenced publication years spectroscopy applied to imetrics scientometrics journal of informetrics and a relevant subset of jasist"

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.DL(Digital Libraries)