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: "neighborhood mixture model for knowledge base completion"
  Abstract: Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete. In this paper, we define a novel entity representation as a mixture of its neighborhood in the knowledge base and apply this technique on TransE-a well-known embedding model for knowledge base completion. Experimental results show that the neighborhood information significantly helps to improve the results of the TransE model, leading to better performance than obtained by other state-of-the-art embedding models on three benchmark datasets for triple classification, entity prediction and relation prediction tasks.

Connected through hyperedge HE-1 (induced by source paper "relational memory based knowledge graph embedding"):
  with members:
    - "relational recurrent neural networks"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "attention is all you need"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "convolutional 2d knowledge graph embeddings"
    - "complex embeddings for simple link prediction"
    - "knowlege graph embedding by flexible translation"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"

Connected through hyperedge HE-2 (induced by source paper "interpreting embedding models of knowledge bases a pedagogical approach"):
  with members:
    - "inducing interpretability in knowledge graph embeddings"
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "analogical inference for multi relational embeddings"
    - "ksr a semantic representation of knowledge graph within a novel unsupervised paradigm"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "knowledge base completion with out of knowledge base entities a graph neural network approach"):
  with members:
    - "gated graph sequence neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"
    - "traversing knowledge graphs in vector space"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "natural language processing for information extraction"):
  with members:
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "classifying relations by ranking with convolutional neural networks"
    - "a hierarchical distance dependent bayesian model for event coreference resolution"
    - "traversing knowledge graphs in vector space"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "a semantic matching energy function for learning with multi relational data"

Connected through hyperedge HE-5 (induced by source paper "an overview of embedding models of entities and relationships for knowledge base completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "hypernetwork knowledge graph embeddings"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "recurrent one hop predictions for reasoning over knowledge graphs"
    - "variational knowledge graph reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "interpretable and compositional relation learning by joint training with an autoencoder"
    - "end to end structure aware convolutional networks for knowledge base completion"

Connected through hyperedge HE-6 (induced by source paper "a novel embedding model for knowledge base completion based on convolutional neural network"):
  with members:
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "search personalization with embeddings"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "convolutional 2d knowledge graph embeddings"
    - "question answering over knowledge base with neural attention combining global knowledge information"
    - "link prediction using embedded knowledge graphs"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"

Connected through hyperedge HE-7 (induced by source paper "an interpretable knowledge transfer model for knowledge base completion"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "lightrnn memory and computation efficient recurrent neural networks"
    - "link prediction using embedded knowledge graphs"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "transfer learning for low resource neural machine translation"
    - "sparse overcomplete word vector representations"
    - "traversing knowledge graphs in vector space"

Connected through hyperedge HE-8 (induced by source paper "search personalization with embeddings"):
  with members:
    - "personalised query suggestion for intranet search with temporal user profiling"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"

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)