human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on2vec embedding based relation prediction for ontology population"):
  Abstract: Populating ontology graphs represents a long-standing problem for the Semantic Web community. Recent advances in translation-based graph embedding methods for populating instance-level knowledge graphs lead to promising new approaching for the ontology population problem. However, unlike instance-level graphs, the majority of relation facts in ontology graphs come with comprehensive semantic relations, which often include the properties of transitivity and symmetry, as well as hierarchical relations. These comprehensive relations are often too complex for existing graph embedding methods, and direct application of such methods is not feasible. Hence, we propose On2Vec, a novel translation-based graph embedding method for ontology population. On2Vec integrates two model components that effectively characterize comprehensive relation facts in ontology graphs. The first is the Component-specific Model that encodes concepts and relations into low-dimensional embedding spaces without a loss of relational properties; the second is the Hierarchy Model that performs focused learning of hierarchical relation facts. Experiments on several well-known ontology graphs demonstrate the promising capabilities of On2Vec in predicting and verifying new relation facts. These promising results also make possible significant improvements in related methods.

Includes members:
  Member "conceptnet 5 5 an open multilingual graph of general knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "multilingual knowledge graph embeddings for cross lingual knowledge alignment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "stranse a novel embedding model of entities and relationships in knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "locally adaptive translation for knowledge graph embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "kgvec2go knowledge graph embeddings as a service")
  Member "holographic embeddings of knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge graphs")
  Member "a semantic matching energy function for learning with multi relational data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint embedding learning of educational knowledge graphs")
  Member "exact solutions to the nonlinear dynamics of learning in deep linear neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")

Question: Please tell me which class the source paper 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)