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: "neural multi step reasoning for question answering on semi structured tables"
  Abstract: Advances in natural language processing tasks have gained momentum in recent years due to the increasingly popular neural network methods. In this paper, we explore deep learning techniques for answering multi-step reasoning questions that operate on semi-structured tables. Challenges here arise from the level of logical compositionality expressed by questions, as well as the domain openness. Our approach is weakly supervised, trained on question-answer-table triples without requiring intermediate strong supervision. It performs two phases: first, machine understandable logical forms (programs) are generated from natural language questions following the work of [Pasupat and Liang, 2015]. Second, paraphrases of logical forms and questions are embedded in a jointly learned vector space using word and character convolutional neural networks. A neural scoring function is further used to rank and retrieve the most probable logical form (interpretation) of a question. Our best single model achieves 34.8% accuracy on the WikiTableQuestions dataset, while the best ensemble of our models pushes the state-of-the-art score on this task to 38.7%, thus slightly surpassing both the engineered feature scoring baseline, as well as the Neural Programmer model of [Neelakantan et al., 2016].

Connected through hyperedge HE-1 (induced by source paper "plotqa reasoning over scientific plots"):
  with members:
    - "towards vqa models that can read"
    - "bilinear attention networks"
    - "dvqa understanding data visualizations via question answering"
    - "scatteract automated extraction of data from scatter plots"
    - "figureqa an annotated figure dataset for visual reasoning"
    - "mask r cnn"
    - "hadamard product for low rank bilinear pooling"
    - "training recurrent answering units with joint loss minimization for vqa"

Connected through hyperedge HE-2 (induced by source paper "learning semantic parsers from denotations with latent structured alignments and abstract programs"):
  with members:
    - "learning to infer program sketches"
    - "learning to generalize from sparse and underspecified rewards"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "semantic parsing with syntax and table aware sql generation"
    - "allennlp a deep semantic natural language processing platform"
    - "decoupling structure and lexicon for zero shot semantic parsing"

Connected through hyperedge HE-3 (induced by source paper "a hierarchical model for data to text generation"):
  with members:
    - "data to text generation with entity modeling"
    - "copy mechanism and tailored training for character based data to text generation"
    - "table2vec neural word and entity embeddings for table population and retrieval"
    - "tabvec table vectors for classification of web tables"
    - "learning neural templates for text generation"
    - "neural arithmetic logic units"
    - "a call for clarity in reporting bleu scores"
    - "data to text generation with content selection and planning"

Connected through hyperedge HE-4 (induced by source paper "merging weak and active supervision for semantic parsing"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "semantic parsing with syntax and table aware sql generation"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "weakly supervised neural semantic parsing with a generative ranker"
    - "typesql knowledge based type aware neural text to sql generation"

Connected through hyperedge HE-5 (induced by source paper "data interpretation over plots"):
  with members:
    - "dvqa understanding data visualizations via question answering"
    - "scatteract automated extraction of data from scatter plots"
    - "a simple neural network module for relational reasoning"
    - "mask r cnn"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "ssd single shot multibox detector"
    - "a diagram is worth a dozen images"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-6 (induced by source paper "question generation from sql queries improves neural semantic parsing"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "semantic parsing with syntax and table aware sql generation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "learning to ask neural question generation for reading comprehension"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "two stage synthesis networks for transfer learning in machine comprehension"
    - "learning to paraphrase for question answering"

Connected through hyperedge HE-7 (induced by source paper "translating natural language to sql using pointer generator networks and how decoding order matters"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "the natural language decathlon multitask learning as question answering"
    - "natural language to structured query generation via meta learning"
    - "incsql training incremental text to sql parsers with non deterministic oracles"
    - "typesql knowledge based type aware neural text to sql generation"
    - "coarse to fine decoding for neural semantic parsing"
    - "robust text to sql generation with execution guided decoding"

Connected through hyperedge HE-8 (induced by source paper "decision support for the masses by enabling conversations with open data"):
  with members:
    - "the gangawatch mobile app to enable usage of water data in every day decisions integrating historical and real time sensing data"
    - "a measure for dialog complexity and its application in streamlining service operations"
    - "ethical challenges in data driven dialogue systems"
    - "learning end to end goal oriented dialog"
    - "compositional semantic parsing on semi structured tables"

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)