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: "thinking fast and slow combining vector spaces and knowledge graphs"
  Abstract: Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses. Vector space models excel at determining similarity between concepts, but are severely constrained when evaluating complex dependency relations and other logic-based operations that are a strength of knowledge graphs. We describe the VKG structure that helps unify knowledge graphs and vector representation of entities, and enables powerful inference methods and search capabilities that combine their complementary strengths. We analogize this to thinking `fast' in vector space along with thinking 'slow' and `deeply' by reasoning over the knowledge graph. We have created a query processing engine that takes complex queries and decomposes them into subqueries optimized to run on the respective knowledge graph or vector view of a VKG. We show that the VKG structure can process specific queries that are not efficiently handled by vector spaces or knowledge graphs alone. We also demonstrate and evaluate the VKG structure and the query processing engine by developing a system called Cyber-All-Intel for knowledge extraction, representation and querying in an end-to-end pipeline grounded in the cybersecurity informatics domain.     arXiv:1708.03310v2 [cs.AI]

Connected through hyperedge HE-1 (induced by source paper "relext relation extraction using deep learning approaches for cybersecurity knowledge graph improvement"):
  with members:
    - "relation extraction a survey"
    - "towards a relation extraction framework for cyber security concepts"
    - "efficient estimation of word representations in vector space"
    - "automatic labeling for entity extraction in cyber security"

Connected through hyperedge HE-2 (induced by source paper "using deep neural networks to translate multi lingual threat intelligence"):
  with members:
    - "bridging neural machine translation and bilingual dictionaries"
    - "neural machine translation of rare words with subword units"
    - "embedding word similarity with neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "preventing poisoning attacks on ai based threat intelligence systems"):
  with members:
    - "summoning demons the pursuit of exploitable bugs in machine learning"
    - "tweetcred real time credibility assessment of content on twitter"
    - "ontological approach toward cybersecurity in cloud computing"

Connected through hyperedge HE-4 (induced by source paper "mining threat intelligence about open source projects and libraries from code repository issues and bug reports"):
  with members:
    - "ontological approach toward cybersecurity in cloud computing"

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)