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: "equation parsing mapping sentences to grounded equations"
  Abstract: Identifying mathematical relations expressed in text is essential to understanding a broad range of natural language text from election reports, to financial news, to sport commentaries to mathematical word problems. This paper focuses on identifying and understanding mathematical relations described within a single sentence. We introduce the problem of Equation Parsing -- given a sentence, identify noun phrases which represent variables, and generate the mathematical equation expressing the relation described in the sentence. We introduce the notion of projective equation parsing and provide an efficient algorithm to parse text to projective equations. Our system makes use of a high precision lexicon of mathematical expressions and a pipeline of structured predictors, and generates correct equations in $70\%$ of the cases. In $60\%$ of the time, it also identifies the correct noun phrase $\rightarrow$ variables mapping, significantly outperforming baselines. We also release a new annotated dataset for task evaluation.

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"
    - "mapping to declarative knowledge for word problem solving"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"

Connected through hyperedge HE-2 (induced by source paper "topiceq a joint topic and mathematical equation model for scientific texts"):
  with members:
    - "topic compositional neural language model"
    - "a neural knowledge language model"
    - "topically driven neural language model"
    - "discovering discrete latent topics with neural variational inference"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "image to markup generation with coarse to fine attention"
    - "exploring the limits of language modeling"
    - "neural variational inference for text processing"

Connected through hyperedge HE-3 (induced by source paper "automatic generation of headlines for online math questions"):
  with members:
    - "pattern generation strategies for improving recognition of handwritten mathematical expressions"
    - "self attentive model for headline generation"
    - "mathematics content understanding for cyberlearning via formula evolution map"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "attention is all you need"
    - "preliminary exploration of formula embedding for mathematical information retrieval can mathematical formulae be embedded like a natural language"
    - "get to the point summarization with pointer generator networks"
    - "image to markup generation with coarse to fine attention"

Connected through hyperedge HE-4 (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"
    - "mapping to declarative knowledge for word problem solving"
    - "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"

Connected through hyperedge HE-5 (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"
    - "mapping to declarative knowledge for word problem solving"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "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-6 (induced by source paper "learning a neural semantic parser from user feedback"):
  with members:
    - "sentence rewriting for semantic parsing"
    - "language to logical form with neural attention"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "distributed representations of words and phrases and their compositionality"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"
    - "learning dependency based compositional semantics"

Connected through hyperedge HE-7 (induced by source paper "program induction by rationale generation learning to solve and explain algebraic word problems"):
  with members:
    - "generating visual explanations"
    - "rationalizing neural predictions"
    - "latent predictor networks for code generation"
    - "solving general arithmetic word problems"
    - "neural programmer inducing latent programs with gradient descent"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "mapping to declarative knowledge for word problem solving"):
  with members:
    - "adversarial examples for evaluating reading comprehension systems"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "question answering via integer programming over semi structured knowledge"
    - "unit dependency graph and its application to arithmetic word problem solving"
    - "solving general arithmetic word problems"
    - "learning dependency based compositional semantics"

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)