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: "applying centrality measures to impact analysis a coauthorship network analysis"
  Abstract: Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties, with the aim to apply centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of twenty years (1988-2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness, betweenness, degree and PageRank) for authors in this network. We find out that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking, and suggest that centrality measures can be useful indicators for impact analysis.

Connected through hyperedge HE-1 (induced by source paper "machine learning in network centrality measures tutorial and outlook"):
  with members:
    - "an analysis of centrality measures for complex and social networks"
    - "a scalable generative graph model with community structure"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "computing classic closeness centrality at scale"
    - "efficient computation of the shapley value for game theoretic network centrality"
    - "representation learning a review and new perspectives"
    - "community structure and scale free collections of erdős renyi graphs"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-2 (induced by source paper "convexity in scientific collaboration networks"):
  with members:
    - "convex skeletons of complex networks"
    - "convexity in complex networks"
    - "constructing bibliometric networks a comparison between full and fractional counting"
    - "distortive effects of initial based name disambiguation on measurements of large scale coauthorship networks"
    - "full and fractional counting in bibliometric networks"
    - "betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks"
    - "co authorship networks in the digital library research community"

Connected through hyperedge HE-3 (induced by source paper "analysis of coauthorship network in political science using centrality measures"):
  with members:
    - "defining and evaluating network communities based on ground truth"
    - "co authorship networks in the digital library research community"

Connected through hyperedge HE-4 (induced by source paper "co contributorship network and division of labor in individual scientific collaborations"):
  with members:
    - "patterns of authors contribution in scientific manuscripts"

Connected through hyperedge HE-5 (induced by source paper "global rank estimation"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "a faster method to estimate closeness centrality ranking"
    - "degree ranking using local information"
    - "literature survey on interplay of topics information diffusion and connections on social networks"
    - "group centrality for semantic networks a swot analysis featuring random walks"
    - "centrality measures in networks based on nodes attributes long range interactions and group influence"
    - "rank me thou shalln t compare me"
    - "understanding spreading patterns on social networks based on network topology"

Connected through hyperedge HE-6 (induced by source paper "a faster method to estimate closeness centrality ranking"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "degree ranking using local information"
    - "rank me thou shalln t compare me"
    - "estimating the degree centrality ranking of a node"
    - "computing classic closeness centrality at scale"
    - "incremental algorithms for network management and analysis based on closeness centrality"
    - "social dynamics of digg"
    - "fast approximation of centrality"

Connected through hyperedge HE-7 (induced by source paper "separation framework an enabler for cooperative and d2d communication for future 5g networks"):
  with members:
    - "d2d enhanced heterogeneous cellular networks with dynamic tdd"
    - "algorithms for enhanced inter cell interference coordination eicic in lte hetnets"
    - "software defined networking a comprehensive survey"
    - "a survey on device to device communication in cellular networks"
    - "what will 5g be"
    - "cache enabled opportunistic cooperative mimo for video streaming in wireless systems"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "hierarchical radio resource optimization for heterogeneous networks with enhanced inter cell interference coordination eicic"

Connected through hyperedge HE-8 (induced by source paper "c 3 index a pagerank based multi faceted metric for authors performance measurement"):
  with members:
    - "on the discovery of success trajectories of authors"
    - "do pagerank based author rankings outperform simple citation counts"
    - "applying weighted pagerank to author citation networks"
    - "are researchers that collaborate more at the international level top performers an investigation on the italian university system"
    - "the inconsistency of the h index"
    - "pagerank for ranking authors in co citation networks"
    - "popular and or prestigious measures of scholarly esteem"

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)