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 "learning analogies and semantic relations"):
  Abstract: We present an algorithm for learning from unlabeled text, based on the #N#Vector Space Model (VSM) of information retrieval, that can solve verbal #N#analogy questions of the kind found in the Scholastic Aptitude Test (SAT). #N#A verbal analogy has the form A:B::C:D, meaning "A is to B as C is to D"; #N#for example, mason:stone::carpenter:wood. SAT analogy questions provide #N#a word pair, A:B, and the problem is to select the most analogous word #N#pair, C:D, from a set of five choices. The VSM algorithm correctly#N#answers 47% of a collection of 374 college-level analogy questions #N#(random guessing would yield 20% correct). We motivate this research by #N#relating it to work in cognitive science and linguistics, and by applying #N#it to a difficult problem in natural language processing, determining #N#semantic relations in noun-modifier pairs. The problem is to classify a #N#noun-modifier pair, such as "laser printer", according to the semantic #N#relation between the noun (printer) and the modifier (laser). We use a #N#supervised nearest-neighbour algorithm that assigns a class to a given #N#noun-modifier pair by finding the most analogous noun-modifier pair in #N#the training data. With 30 classes of semantic relations, on a collection #N#of 600 labeled noun-modifier pairs, the learning algorithm attains an F #N#value of 26.5% (random guessing: 3.3%). With 5 classes of semantic#N#relations, the F value is 43.2% (random: 20%). The performance is #N#state-of-the-art for these challenging problems.

Includes members:
  Member "combining independent modules to solve multiple choice synonym and analogy problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using the web as an implicit training set application to noun compound syntax and semantics")
  Member "mining the web for synonyms pmi ir versus lsa on toefl":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a review of keyphrase extraction")

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.LG(Machine Learning)