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: "policy shaping and generalized update equations for semantic parsing from denotations"
  Abstract: Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e.g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm. We propose effective and general solutions to each of them. Using policy shaping, we bias the search procedure towards semantic parses that are more compatible to the text, which provide better supervision signals for training. In addition, we propose an update equation that generalizes three different families of learning algorithms, which enables fast model exploration. When experimented on a recently proposed sequential question answering dataset, our framework leads to a new state-of-the-art model that outperforms previous work by 5.0% absolute on exact match accuracy.

Connected through hyperedge HE-1 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base"):
  with members:
    - "generating logical forms from graph representations of text and entities"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "neural query language a knowledge base query language for tensorflow"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "neural compositional denotational semantics for question answering"
    - "improving text to sql evaluation methodology"
    - "embedding logical queries on knowledge graphs"
    - "open domain question answering using early fusion of knowledge bases and text"

Connected through hyperedge HE-2 (induced by source paper "introduction to neural network based approaches for question answering over knowledge graphs"):
  with members:
    - "pretrained transformers for simple question answering over knowledge graphs"
    - "learning to generalize from sparse and underspecified rewards"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "practical semantic parsing for spoken language understanding"
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a question answering system over the semantic web"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "neural semantic parsing in low resource settings with back translation and meta learning"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "question generation from sql queries improves neural semantic parsing"
    - "building a neural semantic parser from a domain ontology"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "how to train your maml"
    - "prior knowledge integration for neural machine translation using posterior regularization"

Connected through hyperedge HE-4 (induced by source paper "learning neural sequence to sequence models from weak feedback with bipolar ramp loss"):
  with members:
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "semi supervised qa with generative domain adaptive nets"
    - "coupling distributed and symbolic execution for natural language queries"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-5 (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"
    - "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"
    - "coarse to fine decoding for neural semantic parsing"

Connected through hyperedge HE-6 (induced by source paper "a survey on semantic parsing"):
  with members:
    - "learning programmatic idioms for scalable semantic parsing"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "mapping language to code in programmatic context"
    - "semantic parsing with syntax and table aware sql generation"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"
    - "coarse to fine decoding for neural semantic parsing"

Connected through hyperedge HE-7 (induced by source paper "connecting the dots between mle and rl for sequence generation"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "softmax q distribution estimation for structured prediction a theoretical interpretation for raml"
    - "toward controlled generation of text"
    - "selective encoding for abstractive sentence summarization"
    - "equivalence between policy gradients and soft q learning"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "value based search in execution space for mapping instructions to programs"):
  with members:
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "representation learning for grounded spatial reasoning"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "weakly supervised semantic parsing with abstract examples"

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)