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: "unit dependency graph and its application to arithmetic word problem solving"
  Abstract: Math word problems provide a natural abstraction to a range of natural language understanding problems that involve reasoning about quantities, such as interpreting election results, news about casualties, and the financial section of a newspaper. Units associated with the quantities often provide information that is essential to support this reasoning. This paper proposes a principled way to capture and reason about units and shows how it can benefit an arithmetic word problem solver. This paper presents the concept of Unit Dependency Graphs (UDGs), which provides a compact representation of the dependencies between units of numbers mentioned in a given problem. Inducing the UDG alleviates the brittleness of the unit extraction system and allows for a natural way to leverage domain knowledge about unit compatibility, for word problem solving. We introduce a decomposed model for inducing UDGs with minimal additional annotations, and use it to augment the expressions used in the arithmetic word problem solver of (Roy and Roth 2015) via a constrained inference framework. We show that introduction of UDGs reduces the error of the solver by over 10 %, surpassing all existing systems for solving arithmetic word problems. In addition, it also makes the system more robust to adaptation to new vocabulary and equation forms .

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 "solving arithmetic word problems automatically using transformer and unambiguous representations"):
  with members:
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "data driven methods for solving algebra word problems"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "solving general arithmetic word problems"

Connected through hyperedge HE-3 (induced by source paper "quantity tagger a latent variable sequence labeling approach to solving addition subtraction word problems"):
  with members:
    - "joint detection and location of english puns"
    - "translating a math word problem to an expression tree"
    - "learning cross lingual distributed logical representations for semantic parsing"
    - "neural architectures for named entity recognition"
    - "solving general arithmetic word problems"

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"
    - "a theme rewriting approach for generating algebra word problems"
    - "equation parsing mapping sentences to grounded equations"

Connected through hyperedge HE-5 (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"
    - "equation parsing mapping sentences to grounded equations"
    - "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)