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: "large margin nearest neighbor embedding for knowledge representation"
  Abstract: Traditional way of storing facts in triplets ({\it head\_entity, relation, tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively displayed and easily acquired by mankind, but hardly computed or even reasoned by AI machines. Inspired by the success in applying {\it Distributed Representations} to AI-related fields, recent studies expect to represent each entity and relation with a unique low-dimensional embedding, which is different from the symbolic and atomic framework of displaying knowledge in triplets. In this way, the knowledge computing and reasoning can be essentially facilitated by means of a simple {\it vector calculation}, i.e. ${\bf h} + {\bf r} \approx {\bf t}$. We thus contribute an effective model to learn better embeddings satisfying the formula by pulling the positive tail entities ${\bf t^{+}}$ to get together and close to {\bf h} + {\bf r} ({\it Nearest Neighbor}), and simultaneously pushing the negatives ${\bf t^{-}}$ away from the positives ${\bf t^{+}}$ via keeping a {\it Large Margin}. We also design a corresponding learning algorithm to efficiently find the optimal solution based on {\it Stochastic Gradient Descent} in iterative fashion. Quantitative experiments illustrate that our approach can achieve the state-of-the-art performance, compared with several latest methods on some benchmark datasets for two classical applications, i.e. {\it Link prediction} and {\it Triplet classification}. Moreover, we analyze the parameter complexities among all the evaluated models, and analytical results indicate that our model needs fewer computational resources on outperforming the other methods.

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"
    - "probabilistic belief embedding for knowledge base completion"
    - "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"
    - "probabilistic belief embedding for knowledge base completion"
    - "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"
    - "probabilistic belief embedding for knowledge base completion"
    - "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"
    - "probabilistic belief embedding for knowledge base completion"
    - "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 "locally adaptive translation for knowledge graph embedding"):
  with members:
    - "a semantic matching energy function for learning with multi relational data"

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)