human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "measuring academic influence not all citations are equal"):
  Abstract: The importance of a research article is routinely measured by counting how many times it has been cited. However, treating all citations with equal weight ignores the wide variety of functions that citations perform. We want to automatically identify the subset of references in a bibliography that have a central academic influence on the citing paper. For this purpose, we examine the effectiveness of a variety of features for determining the academic influence of a citation. By asking authors to identify the key references in their own work, we created a data set in which citations were labeled according to their academic influence. Using automatic feature selection with supervised machine learning, we found a model for predicting academic influence that achieves good performance on this data set using only four features. The best features, among those we evaluated, were those based on the number of times a reference is mentioned in the body of a citing paper. The performance of these features inspired us to design an influence-primed h-index (the hip-index). Unlike the conventional h-index, it weights citations by how many times a reference is mentioned. According to our experiments, the hip-index is a better indicator of researcher performance than the conventional h-index.

Includes members:
  Member "applying weighted pagerank to author citation networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "assigning credit to scientific datasets using article citation networks")
  Member "the inconsistency of the h index":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the h index for research assessment simple and popular but shown by mathematical analysis to be inconsistent and misleading")
  Member "weighted citation an indicator of an article s prestige":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "do all citations value the same valuing citations by the value of the citing items")
  Member "from frequency to meaning vector space models of semantics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "popular and or prestigious measures of scholarly esteem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "direct citations between citing publications")
  Member "a principal component analysis of 39 scientific impact measures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the relationship between usage and citations in an open access mega journal")
  Member "scientific paper summarization using citation summary networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a study of human summaries of scientific articles")
  Member "measuring praise and criticism inference of semantic orientation from association":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting paraphrase question generator using pairwise discriminator")

Question: Please tell me which class the source paper 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)