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: "earl joint entity and relation linking for question answering over knowledge graphs"
  Abstract: In order to answer natural language questions over knowledge graphs, most processing pipelines involve entity and relation linking. Traditionally, entity linking and relation linking has been performed either as dependent sequential tasks or independent parallel tasks. In this paper, we propose a framework, calledEARL, which performs entity linking and relation linking as a joint single task. EARL is modeled on an optimised variation of GeneralisedTravelling Salesperson Problem. The system determines the best semantic connection between all keywords of the question by referring to the knowledge graph. This is achieved by exploiting the connection density between entity candidates and relation candidates. We have empirically evaluated the framework on a dataset with 3000 complex questions. Our system surpasses state-of-the-art scores for entity linking task by reporting an accuracy of 0.67against 0.40 from the next best entity linker

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"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "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 "jointly learning explainable rules for recommendation with knowledge graph"):
  with members:
    - "deep reinforcement learning for page wise recommendations"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "outer product based neural collaborative filtering"
    - "explainable recommendation a survey and new perspectives"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"

Connected through hyperedge HE-3 (induced by source paper "message passing for complex question answering over knowledge graphs"):
  with members:
    - "towards a question answering system over the semantic web"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "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-4 (induced by source paper "distantly supervised question parsing"):
  with members:
    - "introduction to neural network based approaches for question answering over knowledge graphs"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "building a conversational agent overnight with dialogue self play"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "practical bayesian optimization in the presence of outliers"
    - "s mart novel tree based structured learning algorithms applied to tweet entity linking"
    - "learning to compose neural networks for question answering"
    - "learning to compose words into sentences with reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "open research knowledge graph next generation infrastructure for semantic scholarly knowledge"):
  with members:
    - "scibert pretrained contextualized embeddings for scientific text"
    - "construction of the literature graph in semantic scholar"
    - "rich semantic models and knowledgebases for highly structured scientific communication"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "growth rates of modern science a bibliometric analysis based on the number of publications and cited references"
    - "micropublications a semantic model for claims evidence arguments and annotations in biomedical communications"
    - "supporting structured browsing for full text scientific research reports"

Connected through hyperedge HE-6 (induced by source paper "structured query construction via knowledge graph embedding"):
  with members:
    - "scef a support confidence aware embedding framework for knowledge graph refinement"
    - "keyword search on rdf graphs a query graph assembly approach"
    - "open world knowledge graph completion"
    - "modeling relation paths for representation learning of knowledge bases"
    - "open question answering with weakly supervised embedding models"
    - "question answering with subgraph embeddings"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (induced by source paper "towards combinational relation linking over knowledge graphs"):
  with members:
    - "graph pattern entity ranking model for knowledge graph completion"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "toruse knowledge graph embedding on a lie group"
    - "explainable entity based recommendations with knowledge graphs"
    - "modeling relation paths for representation learning of knowledge bases"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"

Connected through hyperedge HE-8 (induced by source paper "towards optimisation of collaborative question answering over knowledge graphs"):
  with members:
    - "natural language aggregate query over rdf data"
    - "no one is perfect analysing the performance of question answering components over the dbpedia knowledge graph"
    - "enriching word vectors with subword information"
    - "distributed representations of words and phrases and their compositionality"
    - "fast and accurate annotation of short texts with wikipedia pages"

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.AI(Artificial Intelligence)