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: "annotating derivations a new evaluation strategy and dataset for algebra word problems"
  Abstract: We propose a new evaluation for automatic solvers for algebra word problems, which can identify mistakes that existing evaluations overlook. Our proposal is to evaluate such solvers using derivations, which reflect how an equation system was constructed from the word problem. To accomplish this, we develop an algorithm for checking the equivalence between two derivations, and show how derivation an- notations can be semi-automatically added to existing datasets. To make our experiments more comprehensive, we include the derivation annotation for DRAW-1K, a new dataset containing 1000 general algebra word problems. In our experiments, we found that the annotated derivations enable a more accurate evaluation of automatic solvers than previously used metrics. We release derivation annotations for over 2300 algebra word problems for future evaluations.

Connected through hyperedge HE-1 (induced by source paper "analysing mathematical reasoning abilities of neural models"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "relational recurrent neural networks"
    - "can neural networks understand logical entailment"
    - "attention is all you need"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "adaptive computation time for recurrent neural networks"
    - "learning continuous semantic representations of symbolic expressions"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

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"
    - "mapping to declarative knowledge for word problem solving"
    - "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"
    - "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"
    - "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-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"
    - "mapping to declarative knowledge for word problem solving"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "equation parsing mapping sentences to grounded equations"
    - "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 "numeracy for language models evaluating and improving their ability to predict numbers"):
  with members:
    - "a joint model for question answering and question generation"
    - "auxiliary objectives for neural error detection models"
    - "a neural knowledge language model"
    - "gated word character recurrent language model"
    - "neural text generation from structured data with application to the biography domain"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "clinical text prediction with numerically grounded conditional language models"
    - "solving general arithmetic word problems"

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)