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: "a capsule network based embedding model for knowledge graph completion and search personalization"
  Abstract: In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object). Our CapsE represents each triple as a 3-column matrix where each column vector represents the embedding of an element in the triple. This 3-column matrix is then fed to a convolution layer where multiple filters are operated to generate different feature maps. These feature maps are reconstructed into corresponding capsules which are then routed to another capsule to produce a continuous vector. The length of this vector is used to measure the plausibility score of the triple. Our proposed CapsE obtains better performance than previous state-of-the-art embedding models for knowledge graph completion on two benchmark datasets WN18RR and FB15k-237, and outperforms strong search personalization baselines on SEARCH17.

Connected through hyperedge HE-1 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis"):
  with members:
    - "analyzing knowledge graph embedding methods from a multi embedding interaction perspective"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "tucker tensor factorization for knowledge graph completion"
    - "interaction embeddings for prediction and explanation in knowledge graphs"
    - "on evaluating embedding models for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "toruse knowledge graph embedding on a lie group"

Connected through hyperedge HE-2 (induced by source paper "orthogonal relation transforms with graph context modeling for knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "learning attention based embeddings for relation prediction in knowledge graphs"
    - "tucker tensor factorization for knowledge graph completion"
    - "fast graph representation learning with pytorch geometric"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "probability calibration for knowledge graph embedding models"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "learning attention based embeddings for relation prediction in knowledge graphs"
    - "tucker tensor factorization for knowledge graph completion"
    - "quaternion knowledge graph embedding"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "differentiating concepts and instances for knowledge graph embedding"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-4 (induced by source paper "a re evaluation of knowledge graph completion methods"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "learning attention based embeddings for relation prediction in knowledge graphs"
    - "tucker tensor factorization for knowledge graph completion"
    - "on the knowledge graph completion using translation based embedding the loss is as important as the score"
    - "toward understanding the effect of loss function on then performance of knowledge graph embedding"
    - "dolores deep contextualized knowledge graph embeddings"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "learning hierarchy aware knowledge graph embeddings for link prediction"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "ernie enhanced language representation with informative entities"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"

Connected through hyperedge HE-6 (induced by source paper "limitations of routing by agreement based capsule networks"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "videocapsulenet a simplified network for action detection"
    - "investigating capsule networks with dynamic routing for text classification"
    - "fast capsnet for lung cancer screening"
    - "sparse unsupervised capsules generalize better"
    - "dynamic routing between capsules"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-7 (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"
    - "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"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

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)