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: "jointly embedding relations and mentions for knowledge population"
  Abstract: This paper contributes a joint embedding model for predicting relations between a pair of entities in the scenario of relation inference. It differs from most stand-alone approaches which separately operate on either knowledge bases or free texts. The proposed model simultaneously learns low-dimensional vector representations for both triplets in knowledge repositories and the mentions of relations in free texts, so that we can leverage the evidence both resources to make more accurate predictions. We use NELL to evaluate the performance of our approach, compared with cutting-edge methods. Results of extensive experiments show that our model achieves significant improvement on relation extraction.

Connected through hyperedge HE-1 (induced by source paper "relational word embeddings"):
  with members:
    - "integrating multiplicative features into supervised distributional methods for lexical entailment"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"
    - "neural latent relational analysis to capture lexical semantic relations in a vector space"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "specialising word vectors for lexical entailment"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"

Connected through hyperedge HE-2 (induced by source paper "modeling semantic relatedness using global relation vectors"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "siamese cbow optimizing word embeddings for sentence representations"
    - "learning distributed representations of sentences from unlabelled data"
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "classifying relations by ranking with convolutional neural networks"
    - "distributed representations of sentences and documents"
    - "connecting language and knowledge bases with embedding models for relation extraction"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (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"
    - "probabilistic belief embedding for knowledge base completion"
    - "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-4 (induced by source paper "image annotation incorporating low rankness tag and visual correlation and inhomogeneous errors"):
  with members:
    - "probabilistic belief embedding for knowledge base completion"
    - "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-5 (induced by source paper "image tag completion and refinement by subspace clustering and matrix completion"):
  with members:
    - "probabilistic belief embedding for knowledge base completion"
    - "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-6 (induced by source paper "parallel knowledge embedding with mapreduce on a multi core processor"):
  with members:
    - "probabilistic belief embedding for knowledge base completion"
    - "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-7 (induced by source paper "probabilistic belief embedding for knowledge base completion"):
  with members:
    - "learning embedding representations for knowledge inference on imperfect and incomplete repositories"
    - "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.CL(Computation and Language)