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: "no one is perfect analysing the performance of question answering components over the dbpedia knowledge graph"
  Abstract: Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of question answering for user interaction. DBpedia has been the most prominently used knowledge graph in this setting and most approaches currently use a pipeline of processing steps connecting a sequence of components. In this article, we analyse and micro evaluate the behaviour of 29 available QA components for DBpedia knowledge graph that were released by the research community since 2010. As a result, we provide a perspective on collective failure cases, suggest characteristics of QA components that prevent them from performing better and provide future challenges and research directions for the field.

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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "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"
    - "simplequestions nearly solved a new upperbound and baseline approach"

Connected through hyperedge HE-3 (induced by source paper "falcon 2 0 an entity and relation linking tool over wikidata"):
  with members:
    - "context aware entity linking with attentive neural networks on wikidata knowledge graph"
    - "opentapioca lightweight entity linking for wikidata"
    - "neural collective entity linking"
    - "end to end neural entity linking"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "mag a multilingual knowledge base agnostic and deterministic entity linking approach"
    - "deep joint entity disambiguation with local neural attention"
    - "tagme on the fly annotation of short text fragments by wikipedia entities"

Connected through hyperedge HE-4 (induced by source paper "towards optimisation of collaborative question answering over knowledge graphs"):
  with members:
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "natural language aggregate query over rdf data"
    - "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.IR(Information Retrieval)