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: "learning embedding representations for knowledge inference on imperfect and incomplete repositories"
  Abstract: This paper considers the problem of knowledge inference on large-scale imperfect repositories with incomplete coverage by means of embedding entities and relations at the first attempt. We propose IIKE (Imperfect and Incomplete Knowledge Embedding), a probabilistic model which measures the probability of each belief, i.e. $\langle h,r,t\rangle$, in large-scale knowledge bases such as NELL and Freebase, and our objective is to learn a better low-dimensional vector representation for each entity ($h$ and $t$) and relation ($r$) in the process of minimizing the loss of fitting the corresponding confidence given by machine learning (NELL) or crowdsouring (Freebase), so that we can use $||{\bf h} + {\bf r} - {\bf t}||$ to assess the plausibility of a belief when conducting inference. We use subsets of those inexact knowledge bases to train our model and test the performances of link prediction and triplet classification on ground truth beliefs, respectively. The results of extensive experiments show that IIKE achieves significant improvement compared with the baseline and state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "cr gan learning complete representations for multi view generation"):
  with members:
    - "a survey of multi view representation learning"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "multi view image generation from a single view"
    - "improved training of wasserstein gans"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "facenet a unified embedding for face recognition and clustering"

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"
    - "probabilistic belief embedding for knowledge base completion"
    - "large margin nearest neighbor embedding for knowledge representation"
    - "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"
    - "probabilistic belief embedding for knowledge base completion"
    - "large margin nearest neighbor embedding for knowledge representation"
    - "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"
    - "probabilistic belief embedding for knowledge base completion"
    - "large margin nearest neighbor embedding for knowledge representation"
    - "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:
    - "probabilistic belief embedding for knowledge base completion"
    - "errata distant supervision for relation extraction with matrix completion"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "probabilistic belief embedding for knowledge base completion"):
  with members:
    - "jointly embedding relations and mentions for knowledge population"
    - "connecting language and knowledge bases with embedding models for relation extraction"
    - "distributed representations of words and phrases and their compositionality"

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)