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: "mapping to declarative knowledge for word problem solving"
  Abstract: Math word problems form a natural abstraction to a range of quantitative reasoning problems, such as understanding financial news, sports results, and casualties of war. Solving such problems requires the understanding of several mathematical concepts such as dimensional analysis, subset relationships, etc. In this paper, we develop declarative rules which govern the translation of natural language description of these concepts to math expressions. We then present a framework for incorporating such declarative knowledge into word problem solving. Our method learns to map arithmetic word problem text to math expressions, by learning to select the relevant declarative knowledge for each operation of the solution expression. This provides a way to handle multiple concepts in the same problem while, at the same time, support interpretability of the answer expression. Our method models the mapping to declarative knowledge as a latent variable, thus removing the need for expensive annotations. Experimental evaluation suggests that our domain knowledge based solver outperforms all other systems, and that it generalizes better in the realistic case where the training data it is exposed to is biased in a different way than the test data.

Connected through hyperedge HE-1 (induced by source paper "text2math end to end parsing text into math expressions"):
  with members:
    - "joint detection and location of english puns"
    - "translating a math word problem to an expression tree"
    - "semantic parsing with syntax and table aware sql generation"
    - "learning cross lingual distributed logical representations for semantic parsing"
    - "coarse to fine decoding for neural semantic parsing"
    - "a meaning based statistical english math word problem solver"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "language to logical form with neural attention"

Connected through hyperedge HE-2 (induced by source paper "mathqa towards interpretable math word problem solving with operation based formalisms"):
  with members:
    - "a meaning based statistical english math word problem solver"
    - "learning structured natural language representations for semantic parsing"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "neural generation of regular expressions from natural language with minimal domain knowledge"
    - "annotating derivations a new evaluation strategy and dataset for algebra word problems"
    - "adam a method for stochastic optimization"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-3 (induced by source paper "the gap of semantic parsing a survey on automatic math word problem solvers"):
  with members:
    - "data driven methods for solving algebra word problems"
    - "semantically aligned equation generation for solving and reasoning math word problems"
    - "a structured self attentive sentence embedding"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "a diagram is worth a dozen images"
    - "unit dependency graph and its application to arithmetic word problem solving"
    - "a theme rewriting approach for generating algebra word problems"
    - "equation parsing mapping sentences to grounded equations"

Connected through hyperedge HE-4 (induced by source paper "semantically aligned equation generation for solving and reasoning math word problems"):
  with members:
    - "data driven methods for solving algebra word problems"
    - "fasttext zip compressing text classification models"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "equation parsing mapping sentences to grounded equations"
    - "annotating derivations a new evaluation strategy and dataset for algebra word problems"
    - "solving general arithmetic word problems"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "translating a math word problem to an expression tree"):
  with members:
    - "the gap of semantic parsing a survey on automatic math word problem solvers"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "solving general arithmetic word problems"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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.CL(Computation and Language)