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: "a theme rewriting approach for generating algebra word problems"
  Abstract: Texts present coherent stories that have a particular theme or overall setting, for example science fiction or western. In this paper, we present a text generation method called {\it rewriting} that edits existing human-authored narratives to change their theme without changing the underlying story. We apply the approach to math word problems, where it might help students stay more engaged by quickly transforming all of their homework assignments to the theme of their favorite movie without changing the math concepts that are being taught. Our rewriting method uses a two-stage decoding process, which proposes new words from the target theme and scores the resulting stories according to a number of factors defining aspects of syntactic, semantic, and thematic coherence. Experiments demonstrate that the final stories typically represent the new theme well while still testing the original math concepts, outperforming a number of baselines. We also release a new dataset of human-authored rewrites of math word problems in several themes.

Connected through hyperedge HE-1 (induced by source paper "counterfactual story reasoning and generation"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "bertscore evaluating text generation with bert"
    - "probing neural network comprehension of natural language arguments"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "toward controlled generation of text"

Connected through hyperedge HE-2 (induced by source paper "a multi language platform for generating algebraic mathematical word problems"):
  with members:
    - "neural text generation past present and beyond"
    - "automatic conditional generation of personalized social media short texts"
    - "adversarial text generation via feature mover s distance"
    - "an analysis of neural language modeling at multiple scales"
    - "salsa text self attentive latent space based adversarial text generation"
    - "language gans falling short"
    - "on the state of the art of evaluation in neural language models"
    - "adversarial ranking for language generation"

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"
    - "equation parsing mapping sentences to grounded equations"

Connected through hyperedge HE-4 (induced by source paper "paper abstract writing through editing mechanism"):
  with members:
    - "flexible and creative chinese poetry generation using neural memory"
    - "get to the point summarization with pointer generator networks"
    - "table to text generation by structure aware seq2seq learning"
    - "neural network based abstract generation for opinions and arguments"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural text generation from structured data with application to the biography domain"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "steering output style and topic in neural response generation"):
  with members:
    - "a persona based neural conversation model"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "controlling output length in neural encoder decoders"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "topic aware neural response generation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "predicting the quality of short narratives from social media"):
  with members:
    - "automatic text scoring using neural networks"
    - "inferring interpersonal relations in narrative summaries"
    - "can cascades be predicted"
    - "distributed representations of words and phrases and their compositionality"

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)