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 "quantity tagger a latent variable sequence labeling approach to solving addition subtraction word problems"):
  Abstract: An arithmetic word problem typically includes a textual description containing several constant quantities. The key to solving the problem is to reveal the underlying mathematical relations (such as addition and subtraction) among quantities, and then generate equations to find solutions. This work presents a novel approach, Quantity Tagger, that automatically discovers such hidden relations by tagging each quantity with a sign corresponding to one type of mathematical operation. For each quantity, we assume there exists a latent, variable-sized quantity span surrounding the quantity token in the text, which conveys information useful for determining its sign. Empirical results show that our method achieves 5 and 8 points of accuracy gains on two datasets respectively, compared to prior approaches.

Includes members:
  Member "joint detection and location of english puns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mining commonsense facts from the physical world")
  Member "translating a math word problem to an expression tree":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "text2math end to end parsing text into math expressions")
  Member "learning cross lingual distributed logical representations for semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mining commonsense facts from the physical world")
  Member "unit dependency graph and its application to arithmetic word problem solving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "text2math end to end parsing text into math expressions")
  Member "neural architectures for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge graphs")
  Member "solving general arithmetic word problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "equate a benchmark evaluation framework for quantitative reasoning in natural language inference")

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.CL(Computation and Language)