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: "modeling semantics with gated graph neural networks for knowledge base question answering"
  Abstract: The most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we address the problem of learning vector representations for complex semantic parses that consist of multiple entities and relations. Previous work largely focused on selecting the correct semantic relations for a question and disregarded the structure of the semantic parse: the connections between entities and the directions of the relations. We propose to use Gated Graph Neural Networks to encode the graph structure of the semantic parse. We show on two data sets that the graph networks outperform all baseline models that do not explicitly model the structure. The error analysis confirms that our approach can successfully process complex semantic parses.

Connected through hyperedge HE-1 (induced by source paper "knowledge based conversational search"):
  with members:
    - "message passing for complex question answering over knowledge graphs"
    - "query expansion techniques for information retrieval a survey"
    - "open data chatbot"
    - "towards a question answering system over the semantic web"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "interpretation of natural language rules in conversational machine reading"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "dialogue generation from imitation learning to inverse reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "augmenting neural machine translation with knowledge graphs"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
    - "machine translation using semantic web technologies a survey"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "learning word vectors for 157 languages"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "tripartite heterogeneous graph propagation for large scale social recommendation"):
  with members:
    - "graph neural networks for social recommendation"
    - "representation learning for attributed multiplex heterogeneous network"
    - "graph convolutional neural networks for web scale recommender systems"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "pitfalls of graph neural network evaluation"
    - "applying deep learning to airbnb search"
    - "representation learning on graphs with jumping knowledge networks"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "variational reasoning for question answering with knowledge graph"

Connected through hyperedge HE-5 (induced by source paper "representing schema structure with graph neural networks for text to sql parsing"):
  with members:
    - "grammar based neural text to sql generation"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "allennlp a deep semantic natural language processing platform"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "learning a neural semantic parser from user feedback"
    - "abstract syntax networks for code generation and semantic parsing"

Connected through hyperedge HE-6 (induced by source paper "message passing for complex question answering over knowledge graphs"):
  with members:
    - "towards a question answering system over the semantic web"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "magnitude a fast efficient universal vector embedding utility package"
    - "embedding logical queries on knowledge graphs"
    - "relational inductive biases deep learning and graph networks"
    - "learning to rank query graphs for complex question answering over knowledge graphs"
    - "no one is perfect analysing the performance of question answering components over the dbpedia knowledge graph"
    - "simplequestions nearly solved a new upperbound and baseline approach"

Connected through hyperedge HE-7 (induced by source paper "global reasoning over database structures for text to sql parsing"):
  with members:
    - "grammar based neural text to sql generation"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "improving neural parsing by disentangling model combination and reranking effects"
    - "abstract syntax networks for code generation and semantic parsing"

Connected through hyperedge HE-8 (induced by source paper "gated graph recursive neural networks for molecular property prediction"):
  with members:
    - "link prediction based on graph neural networks"
    - "semi supervised user geolocation via graph convolutional networks"
    - "neural message passing for quantum chemistry"
    - "moleculenet a benchmark for molecular machine learning"
    - "language modeling with gated convolutional networks"
    - "interaction networks for learning about objects relations and physics"
    - "convolutional networks on graphs for learning molecular fingerprints"

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)