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: "coupling distributed and symbolic execution for natural language queries"
  Abstract: Building neural networks to query a knowledge base (a table) with natural language is an emerging research topic in deep learning. An executor for table querying typically requires multiple steps of execution because queries may have complicated structures. In previous studies, researchers have developed either fully distributed executors or symbolic executors for table querying. A distributed executor can be trained in an end-to-end fashion, but is weak in terms of execution efficiency and explicit interpretability. A symbolic executor is efficient in execution, but is very difficult to train especially at initial stages. In this paper, we propose to couple distributed and symbolic execution for natural language queries, where the symbolic executor is pretrained with the distributed executor's intermediate execution results in a step-by-step fashion. Experiments show that our approach significantly outperforms both distributed and symbolic executors, exhibiting high accuracy, high learning efficiency, high execution efficiency, and high interpretability.

Connected through hyperedge HE-1 (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"
    - "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"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-2 (induced by source paper "has qa hierarchical answer spans model for open domain question answering"):
  with members:
    - "reinforced mnemonic reader for machine comprehension"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "quasar datasets for question answering by search and reading"
    - "making neural qa as simple as possible but not simpler"
    - "ask the right questions active question reformulation with reinforcement learning"
    - "s net from answer extraction to answer generation for machine reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "an imitation learning approach to unsupervised parsing"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "reinforcement learning from imperfect demonstrations"
    - "on tree based neural sentence modeling"
    - "backpropagating through structured argmax using a spigot"
    - "hypothesis only baselines in natural language inference"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "grammar induction with neural language models an unusual replication"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"

Connected through hyperedge HE-4 (induced by source paper "text classification for azerbaijani language using machine learning and embedding"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning a critical appraisal"
    - "a structured self attentive sentence embedding"
    - "rationalizing neural predictions"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "semantic parsing with syntax and table aware sql generation"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "inferring and executing programs for visual reasoning"
    - "bidirectional attention for sql generation"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "learning to reason end to end module networks for visual question answering"
    - "weakly supervised semantic parsing with abstract examples"
    - "type and content driven synthesis of sql queries from natural language"

Connected through hyperedge HE-6 (induced by source paper "seq2sql generating structured queries from natural language using reinforcement learning"):
  with members:
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "learning a neural semantic parser from user feedback"
    - "language to logical form with neural attention"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural combinatorial optimization with reinforcement learning"
    - "joint learning of ontology and semantic parser from text"
    - "learning a natural language interface with neural programmer"

Connected through hyperedge HE-7 (induced by source paper "an interpretable reasoning network for multi relation question answering"):
  with members:
    - "reasonet learning to stop reading in machine comprehension"
    - "key value memory networks for directly reading documents"
    - "learning to compose neural networks for question answering"
    - "simple question answering by attentive convolutional neural network"
    - "question answering on freebase via relation extraction and textual evidence"
    - "end to end memory networks"
    - "neural programmer inducing latent programs with gradient descent"
    - "neural enquirer learning to query tables with natural language"

Connected through hyperedge HE-8 (induced by source paper "learning to generate structured queries from natural language with indirect supervision"):
  with members:
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "typesql knowledge based type aware neural text to sql generation"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "learning a neural semantic parser from user feedback"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "modeling coverage for neural machine translation"
    - "dynamic coattention networks for question answering"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

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.LG(Machine Learning)