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: "algorithmically generated subject categories based on citation relations an empirical micro study using papers on overall water splitting"
  Abstract: Abstract   One important reason for the use of field categorization in bibliometrics is the necessity to make citation impact of papers published in different scientific fields comparable with each other. Raw citations are normalized by using field-categorization schemes to achieve comparable citation scores. There are different approaches to field categorization available. They can be broadly classified as intellectual and algorithmic approaches. A paper-based algorithmically constructed classification system (ACCS) was proposed which is based on citation relations. Using a few ACCS field-specific clusters, we investigate the discriminatory power of the ACCS. The micro study focusses on the topic ‘overall water splitting’ and related topics. The first part of the study investigates intellectually whether the ACCS is able to identify papers on overall water splitting reliably and validly. Next, we compare the ACCS with (1) a paper-based intellectual (INSPEC) classification and (2) a journal-based intellectual classification (Web of Science, WoS, subject categories). In the last part of our case study, we compare the average number of citations in selected ACCS clusters (on overall water splitting and related topics) with the average citation count of publications in WoS subject categories related to these clusters. The results of this micro study question the discriminatory power of the ACCS. We recommend larger follow-up studies on broad datasets.

Connected through hyperedge HE-1 (induced by source paper "practical method to reclassify web of science articles into unique subject categories and broad disciplines"):
  with members:
    - "a principled methodology for comparing relatedness measures for clustering publications"
    - "granularity of algorithmically constructed publication level classifications of research publications identification of topics"
    - "which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "large scale analysis of the accuracy of the journal classification systems of web of science and scopus"
    - "the operationalization of fields as wos subject categories wcs in evaluative bibliometrics the cases of library and information science and science technology studies"
    - "a new methodology for constructing a publication level classification system of science"

Connected through hyperedge HE-2 (induced by source paper "how to interpret algorithmically constructed topical structures of research specialties a case study comparing an internal and an external mapping of the topical structure of invasion biology"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "granularity of algorithmically constructed publication level classifications of research publications identification of topics"
    - "which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge"
    - "mutual information based labelling and comparing clusters"
    - "memetic search for overlapping topics based on a local evaluation of link communities"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "a new methodology for constructing a publication level classification system of science"

Connected through hyperedge HE-3 (induced by source paper "a principled methodology for comparing relatedness measures for clustering publications"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "granularity of algorithmically constructed publication level classifications of research publications identification of topics"
    - "which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge"
    - "constructing bibliometric networks a comparison between full and fractional counting"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "citnetexplorer a new software tool for analyzing and visualizing citation networks"
    - "a new methodology for constructing a publication level classification system of science"

Connected through hyperedge HE-4 (induced by source paper "critical rationalism and the search for standard field normalized indicators in bibliometrics"):
  with members:
    - "relative citation ratio rcr an empirical attempt to study a new field normalized bibliometric indicator"
    - "citation score normalized by cited references csncr the introduction of a new citation impact indicator"
    - "a review of the literature on citation impact indicators"
    - "how are excellent highly cited papers defined in bibliometrics a quantitative analysis of the literature"
    - "methods for the generation of normalized citation impact scores in bibliometrics which method best reflects the judgements of experts"
    - "alternative metrics in scientometrics a meta analysis of research into three altmetrics"
    - "measuring impact in research evaluations a thorough discussion of methods for effects of and problems with impact measurements"
    - "on the causes of subject specific citation rates in web of science"

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)