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: "program induction by rationale generation learning to solve and explain algebraic word problems"
  Abstract: Solving algebraic word problems requires executing a series of arithmetic operations---a program---to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones. To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales. Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.

Connected through hyperedge HE-1 (induced by source paper "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"):
  with members:
    - "from recognition to cognition visual commonsense reasoning"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "the web as a knowledge base for answering complex questions"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-2 (induced by source paper "evidence sentence extraction for machine reading comprehension"):
  with members:
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "training a ranking function for open domain question answering"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "neural models for reasoning over multiple mentions using coreference"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "deep probabilistic logic a unifying framework for indirect supervision"

Connected through hyperedge HE-3 (induced by source paper "make up your mind adversarial generation of inconsistent natural language explanations"):
  with members:
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "annotation artifacts in natural language inference data"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "textual explanations for self driving vehicles"
    - "e snli natural language inference with natural language explanations"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-4 (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-5 (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"
    - "language to logical form with neural attention"

Connected through hyperedge HE-6 (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-7 (induced by source paper "putting question answering systems into practice transfer learning for efficient domain customization"):
  with members:
    - "deep learning in business analytics and operations research models applications and managerial implications"
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "adaptive document retrieval for deep question answering"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "reading wikipedia to answer open domain questions"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-8 (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"
    - "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"
    - "annotating derivations a new evaluation strategy and dataset for algebra 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.AI(Artificial Intelligence)