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: "probabilistic belief embedding for knowledge base completion"
  Abstract: This paper contributes a novel embedding model which measures the probability of each belief $\langle h,r,t,m\rangle$ in a large-scale knowledge repository via simultaneously learning distributed representations for entities ($h$ and $t$), relations ($r$), and the words in relation mentions ($m$). It facilitates knowledge completion by means of simple vector operations to discover new beliefs. Given an imperfect belief, we can not only infer the missing entities, predict the unknown relations, but also tell the plausibility of the belief, just leveraging the learnt embeddings of remaining evidences. To demonstrate the scalability and the effectiveness of our model, we conduct experiments on several large-scale repositories which contain millions of beliefs from WordNet, Freebase and NELL, and compare it with other cutting-edge approaches via competing the performances assessed by the tasks of entity inference, relation prediction and triplet classification with respective metrics. Extensive experimental results show that the proposed model outperforms the state-of-the-arts with significant improvements.

Connected through hyperedge HE-1 (induced by source paper "subspace clustering based tag sharing for inductive tag matrix refinement with complex errors"):
  with members:
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "jointly embedding relations and mentions for knowledge population"
    - "large margin nearest neighbor embedding for knowledge representation"
    - "errata distant supervision for relation extraction with matrix completion"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "provable inductive matrix completion"
    - "efficient estimation of word representations in vector space"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-2 (induced by source paper "image annotation incorporating low rankness tag and visual correlation and inhomogeneous errors"):
  with members:
    - "jointly embedding relations and mentions for knowledge population"
    - "large margin nearest neighbor embedding for knowledge representation"
    - "learning embedding representations for knowledge inference on imperfect and incomplete repositories"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "efficient estimation of word representations in vector space"
    - "learning by transduction"
    - "robust principal component analysis"
    - "the google similarity distance"

Connected through hyperedge HE-3 (induced by source paper "image tag completion and refinement by subspace clustering and matrix completion"):
  with members:
    - "jointly embedding relations and mentions for knowledge population"
    - "large margin nearest neighbor embedding for knowledge representation"
    - "learning embedding representations for knowledge inference on imperfect and incomplete repositories"
    - "provable inductive matrix completion"
    - "efficient estimation of word representations in vector space"
    - "robust recovery of subspace structures by low rank representation"
    - "distributed low rank subspace segmentation"

Connected through hyperedge HE-4 (induced by source paper "parallel knowledge embedding with mapreduce on a multi core processor"):
  with members:
    - "jointly embedding relations and mentions for knowledge population"
    - "large margin nearest neighbor embedding for knowledge representation"
    - "learning embedding representations for knowledge inference on imperfect and incomplete repositories"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "jointly embedding relations and mentions for knowledge population"):
  with members:
    - "learning embedding representations for knowledge inference on imperfect and incomplete repositories"
    - "errata distant supervision for relation extraction with matrix completion"
    - "efficient estimation of word representations in vector space"

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)