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: "network based statistical comparison of citation topology of bibliographic databases"
  Abstract: Modern bibliographic databases provide the basis for scientific research and its evaluation. While their content and structure differ substantially, there exist only informal notions on their reliability. Here we compare the topological consistency of citation networks extracted from six popular bibliographic databases including Web of Science, CiteSeer and arXiv.org. The networks are assessed through a rich set of local and global graph statistics. We first reveal statistically significant inconsistencies between some of the databases with respect to individual statistics. For example, the introduced field bow-tie decomposition of DBLP Computer Science Bibliography substantially differs from the rest due to the coverage of the database, while the citation information within arXiv.org is the most exhaustive. Finally, we compare the databases over multiple graph statistics using the critical difference diagram. The citation topology of DBLP Computer Science Bibliography is the least consistent with the rest, while, not surprisingly, Web of Science is significantly more reliable from the perspective of consistency. This work can serve either as a reference for scholars in bibliometrics and scientometrics or a scientific evaluation guideline for governments and research agencies.

Connected through hyperedge HE-1 (induced by source paper "on the use of arxiv as a dataset"):
  with members:
    - "predicting research trends from arxiv"
    - "universal sentence encoder"
    - "relational inductive biases deep learning and graph networks"
    - "graph embedding techniques applications and performance a survey"
    - "representation learning on graphs methods and applications"
    - "document embedding with paragraph vectors"
    - "text segmentation based on semantic word embeddings"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "empirical comparison of network sampling techniques"):
  with members:
    - "sampling promotes community structure in social and information networks"
    - "a survey and taxonomy of graph sampling"
    - "a new algorithm for extracting a small representative subgraph from a very large graph"
    - "benefits of bias towards better characterization of network sampling"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "quantifying the consistency of scientific databases"):
  with members:
    - "model of complex networks based on citation dynamics"
    - "quantifying long term scientific impact"
    - "characterizing interdisciplinarity of researchers and research topics using web search engines"
    - "an expert system for detecting automobile insurance fraud using social network analysis"
    - "betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks"

Connected through hyperedge HE-4 (induced by source paper "sampling promotes community structure in social and information networks"):
  with members:
    - "assessing the effectiveness of real world network simplification"
    - "node mixing and group structure of complex software networks"
    - "benefits of bias towards better characterization of network sampling"
    - "on the bias of bfs"

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)