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: "kbqa learning question answering over qa corpora and knowledge bases"
  Abstract: Question answering (QA) has become a popular way for humans to access billion-scale knowledge bases. Unlike web search, QA over a knowledge base gives out accurate and concise results, provided that natural language questions can be understood and mapped precisely to structured queries over the knowledge base. The challenge, however, is that a human can ask one question in many different ways. Previous approaches have natural limits due to their representations: rule based approaches only understand a small set of "canned" questions, while keyword based or synonym based approaches cannot fully understand the questions. In this paper, we design a new kind of question representation: templates, over a billion scale knowledge base and a million scale QA corpora. For example, for questions about a city's population, we learn templates such as What's the population of $city?, How many people are there in $city?. We learned 27 million templates for 2782 intents. Based on these templates, our QA system KBQA effectively supports binary factoid questions, as well as complex questions which are composed of a series of binary factoid questions. Furthermore, we expand predicates in RDF knowledge base, which boosts the coverage of knowledge base by 57 times. Our QA system beats all other state-of-art works on both effectiveness and efficiency over QALD benchmarks.

Connected through hyperedge HE-1 (induced by source paper "hhh an online medical chatbot system based on knowledge graph and hierarchical bi directional attention"):
  with members:
    - "finding answers from the word of god domain adaptation for neural networks in biblical question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improved neural relation detection for knowledge base question answering"
    - "attention is all you need"
    - "cfo conditional focused neural question answering with large scale knowledge bases"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "dependently typed knowledge graphs"):
  with members:
    - "explainable reasoning over knowledge graphs for recommendation"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-3 (induced by source paper "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"):
  with members:
    - "improving question answering with external knowledge"
    - "understanding dataset design choices for multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "repurposing entailment for multi hop question answering tasks"
    - "an interpretable reasoning network for multi relation question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"

Connected through hyperedge HE-4 (induced by source paper "recent advances in neural question generation"):
  with members:
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a better metric for evaluating question generation systems"
    - "improving neural question generation using answer separation"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "automating reading comprehension by generating question and answer pairs"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "neural generation of diverse questions using answer focus contextual and linguistic features"

Connected through hyperedge HE-5 (induced by source paper "using context information to enhance simple question answering"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "freebase triples a methodology for processing the freebase data dumps"
    - "attention is all you need"
    - "semi supervised multitask learning for sequence labeling"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-6 (induced by source paper "using local knowledge graph construction to scale seq2seq models to multi document inputs"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "transformer xl attentive language models beyond a fixed length context"
    - "lattice cnns for matching based chinese question answering"
    - "adaptive attention span in transformers"
    - "eli5 long form question answering"
    - "graph neural networks a review of methods and applications"
    - "building dynamic knowledge graphs from text using machine reading comprehension"

Connected through hyperedge HE-7 (induced by source paper "kg bert bert for knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "ernie enhanced language representation with informative entities"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "dolores deep contextualized knowledge graph embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "iteratively learning embeddings and rules for knowledge graph reasoning"):
  with members:
    - "improving knowledge graph embedding using simple constraints"
    - "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"

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)