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: "solving general arithmetic word problems"
  Abstract: This paper presents a novel approach to automatically solving arithmetic word problems. This is the first algorithmic approach that can handle arithmetic problems with multiple steps and operations, without depending on additional annotations or predefined templates. We develop a theory for expression trees that can be used to represent and evaluate the target arithmetic expressions; we use it to uniquely decompose the target arithmetic problem to multiple classification problems; we then compose an expression tree, combining these with world knowledge through a constrained inference framework. Our classifiers gain from the use of {\em quantity schemas} that supports better extraction of features. Experimental results show that our method outperforms existing systems, achieving state of the art performance on benchmark datasets of arithmetic word problems.

Connected through hyperedge HE-1 (induced by source paper "equate a benchmark evaluation framework for quantitative reasoning in natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "what knowledge is needed to solve the rte5 textual entailment challenge"
    - "hypothesis only baselines in natural language inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stress test evaluation for natural language inference"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "neural probabilistic logic programming in deepproblog"):
  with members:
    - "neural logic machines"
    - "playgol learning programs through play"
    - "nlprolog reasoning with weak unification for question answering in natural language"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"
    - "neural guided constraint logic programming for program synthesis"
    - "learning explanatory rules from noisy data"

Connected through hyperedge HE-3 (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-4 (induced by source paper "numnet machine reading comprehension with numerical reasoning"):
  with members:
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "what makes reading comprehension questions easier"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "where s my head definition dataset and models for numeric fused heads identification and resolution"):
  with members:
    - "neural coreference resolution with deep biaffine attention by joint mention detection and mention clustering"
    - "numeracy for language models evaluating and improving their ability to predict numbers"
    - "allennlp a deep semantic natural language processing platform"
    - "neural arithmetic logic units"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "anaphora and coreference resolution a review"
    - "deep reinforcement learning for chinese zero pronoun resolution"

Connected through hyperedge HE-6 (induced by source paper "just add functions a neural symbolic language model"):
  with members:
    - "dynamic evaluation of transformer language models"
    - "numeracy for language models evaluating and improving their ability to predict numbers"
    - "deep learning a critical appraisal"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "regularizing and optimizing lstm language models"
    - "a neural knowledge language model"
    - "numerically grounded language models for semantic error correction"

Connected through hyperedge HE-7 (induced by source paper "learning numeral embeddings"):
  with members:
    - "do nlp models know numbers probing numeracy in embeddings"
    - "numeracy for language models evaluating and improving their ability to predict numbers"
    - "ncrf an open source neural sequence labeling toolkit"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "numerically grounded language models for semantic error correction"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (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"
    - "unit dependency graph and its application to arithmetic word problem solving"

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)