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: "variational reasoning for question answering with knowledge graph"
  Abstract: Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts. However, it is challenging to build QA systems which can learn to reason over knowledge graphs based on question-answer pairs alone. First, when people ask questions, their expressions are noisy (for example, typos in texts, or variations in pronunciations), which is non-trivial for the QA system to match those mentioned entities to the knowledge graph. Second, many questions require multi-hop logic reasoning over the knowledge graph to retrieve the answers. To address these challenges, we propose a novel and unified deep learning architecture, and an end-to-end variational learning algorithm which can handle noise in questions, and learn multi-hop reasoning simultaneously. Our method achieves state-of-the-art performance on a recent benchmark dataset in the literature. We also derive a series of new benchmark datasets, including questions for multi-hop reasoning, questions paraphrased by neural translation model, and questions in human voice. Our method yields very promising results on all these challenging datasets.

Connected through hyperedge HE-1 (induced by source paper "differentiable reasoning over a virtual knowledge base"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "neural query language a knowledge base query language for tensorflow"
    - "multi hop paragraph retrieval for open domain question answering"
    - "leveraging knowledge bases in lstms for improving machine reading"

Connected through hyperedge HE-2 (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"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "neural compositional denotational semantics for question answering"
    - "improving text to sql evaluation methodology"
    - "embedding logical queries on knowledge graphs"

Connected through hyperedge HE-3 (induced by source paper "text modeling with syntax aware variational autoencoders"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "treegan syntax aware sequence generation with generative adversarial networks"
    - "variational knowledge graph reasoning"
    - "syntax directed variational autoencoder for structured data"
    - "dirichlet variational autoencoder for text modeling"
    - "linguistically informed self attention for semantic role labeling"
    - "toward controlled generation of text"

Connected through hyperedge HE-4 (induced by source paper "pullnet open domain question answering with iterative retrieval on knowledge bases and text"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "the web as a knowledge base for answering complex questions"
    - "phase conductor on multi layered attentions for machine comprehension"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"

Connected through hyperedge HE-5 (induced by source paper "measuring compositional generalization a comprehensive method on realistic data"):
  with members:
    - "measuring compositionality in representation learning"
    - "learning by abstraction the neural state machine"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "analysing mathematical reasoning abilities of neural models"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "universal transformers"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "improving text to sql evaluation methodology"

Connected through hyperedge HE-6 (induced by source paper "differentiable representations for multihop inference rules"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "neural compositional denotational semantics for question answering"
    - "deep sets"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "tensorlog deep learning meets probabilistic dbs"

Connected through hyperedge HE-7 (induced by source paper "uhop an unrestricted hop relation extraction framework for knowledge based question answering"):
  with members:
    - "knowledge base relation detection via multi view matching"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "an attention based word level interaction model relation detection for knowledge base question answering"
    - "variational knowledge graph reasoning"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "improved neural relation detection for knowledge base question answering"

Connected through hyperedge HE-8 (induced by source paper "multi hop reading comprehension via deep reinforcement learning based document traversal"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "making neural qa as simple as possible but not simpler"
    - "multi mention learning for reading comprehension with neural cascades"
    - "reinforced mnemonic reader for machine reading comprehension"
    - "linguistic knowledge as memory for recurrent neural networks"
    - "reading wikipedia to answer open domain questions"
    - "proximal policy optimization algorithms"
    - "a deep reinforced model for abstractive summarization"

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)