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 "cross lingual sentiment quantification"):
  Abstract: We discuss \emph{Cross-Lingual Text Quantification} (CLTQ), the task of performing text quantification (i.e., estimating the relative frequency $p_{c}(D)$ of all classes $c\in\mathcal{C}$ in a set $D$ of unlabelled documents) when training documents are available for a source language $\mathcal{S}$ but not for the target language $\mathcal{T}$ for which quantification needs to be performed. CLTQ has never been discussed before in the literature; we establish baseline results for the binary case by combining state-of-the-art quantification methods with methods capable of generating cross-lingual vectorial representations of the source and target documents involved. We present experimental results obtained on publicly available datasets for cross-lingual sentiment classification; the results show that the presented methods can perform CLTQ with a surprising level of accuracy.

Includes members:
  Member "revisiting distributional correspondence indexing a python reimplementation and new experiments":
    also connected through other hyperedges:
      (none)
  Member "evaluation measures for quantification an axiomatic approach":
    also connected through other hyperedges:
      (none)
  Member "a recurrent neural network for sentiment quantification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evaluation measures for quantification an axiomatic approach")
  Member "cross lingual adaptation using structural correspondence learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "embedding projection for targeted cross lingual sentiment model comparisons and a real world study")

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)