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: "4d specialty approximation ability to distinguish between related specialties"
  Abstract: Publication and citation patterns can vary significantly between related disciplines or more narrow specialties, even when sharing journals. Journal-based structures are therefore not accurate enough to approximate certain specialties, neither subject categories in global citation indices, nor cell sub-structures (Rons, 2012). This paper presents first test results of a new methodology that approximates the specialty of a highly specialized seed record by combining criteria for four publication metadata-fields, thereby broadly covering conceptual components defining disciplines and scholarly communication. To offer added value compared to journal-based structures, the methodology needs to generate sufficiently distinct results for seed directories in related specialties (sharing subject categories, cells, or even sources) with significantly different characteristics. This is tested successfully for the sub-domains of theoretical and experimental particle physics. In particular analyses of specialties with characteristics deviating from those of a broader discipline embedded in can benefit from an approach discerning down to specialty level. Such specialties are potentially present in all disciplines, for instance as cases of peripheral, emerging, frontier, or strategically prioritized research areas.

Connected through hyperedge HE-1 (induced by source paper "bibliometric approximation of a scientific specialty by combining key sources title words authors and references"):
  with members:
    - "investigation of partition cells as a structural basis suitable for assessments of individual scientists"
    - "the operationalization of fields as wos subject categories wcs in evaluative bibliometrics the cases of library and information science and science technology studies"
    - "detecting the historical roots of research fields by reference publication year spectroscopy rpys"
    - "citation analysis may severely underestimate the impact of clinical research as compared to basic research"
    - "partition based field normalization an approach to highly specialized publication records"
    - "a new methodology for constructing a publication level classification system of science"
    - "the structure and dynamics of co citation clusters a multiple perspective co citation analysis"

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)