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: "improving a neural semantic parser by counterfactual learning from human bandit feedback"
  Abstract: Counterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target system. We show how to apply this learning framework to neural semantic parsing. From a machine learning perspective, the key challenge lies in a proper reweighting of the estimator so as to avoid known degeneracies in counterfactual learning, while still being applicable to stochastic gradient optimization. To conduct experiments with human users, we devise an easy-to-use interface to collect human feedback on semantic parses. Our work is the first to show that semantic parsers can be improved significantly by counterfactual learning from logged human feedback data.

Connected through hyperedge HE-1 (induced by source paper "exploring neural models for parsing natural language into first order logic"):
  with members:
    - "probing neural network comprehension of natural language arguments"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "semantic parsing with syntax and table aware sql generation"
    - "attention is all you need"
    - "found in translation reconstructing phylogenetic language trees from translations"
    - "learning a neural semantic parser from user feedback"
    - "robust incremental neural semantic graph parsing"
    - "a deep generative framework for paraphrase generation"

Connected through hyperedge HE-2 (induced by source paper "don t paraphrase detect rapid and effective data collection for semantic parsing"):
  with members:
    - "learning to learn semantic parsers from natural language supervision"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "end to end open domain question answering with"
    - "practical semantic parsing for spoken language understanding"
    - "building a neural semantic parser from a domain ontology"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "building a conversational agent overnight with dialogue self play"

Connected through hyperedge HE-3 (induced by source paper "learning neural sequence to sequence models from weak feedback with bipolar ramp loss"):
  with members:
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "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-4 (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"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-5 (induced by source paper "interactive text ranking with bayesian optimisation a case study on community qa and summarisation"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "passage ranking with weak supervision"
    - "preference based interactive multi document summarisation"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"
    - "active learning for interactive neural machine translation of data streams"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "commonsense for generative multi hop question answering tasks"

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"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "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"
    - "coarse to fine decoding for neural semantic parsing"

Connected through hyperedge HE-7 (induced by source paper "counterfactual learning from human proofreading feedback for semantic parsing"):
  with members:
    - "coupling distributed and symbolic execution for natural language queries"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

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)