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: "improving knowledge graph embedding using simple constraints"
  Abstract: Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Early works performed this task via simple models developed over KG triples. Recent attempts focused on either designing more complicated triple scoring models, or incorporating extra information beyond triples. This paper, by contrast, investigates the potential of using very simple constraints to improve KG embedding. We examine non-negativity constraints on entity representations and approximate entailment constraints on relation representations. The former help to learn compact and interpretable representations for entities. The latter further encode regularities of logical entailment between relations into their distributed representations. These constraints impose prior beliefs upon the structure of the embedding space, without negative impacts on efficiency or scalability. Evaluation on WordNet, Freebase, and DBpedia shows that our approach is simple yet surprisingly effective, significantly and consistently outperforming competitive baselines. The constraints imposed indeed improve model interpretability, leading to a substantially increased structuring of the embedding space. Code and data are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank"):
  with members:
    - "multi view knowledge graph embedding for entity alignment"
    - "knowledge graph entity alignment with graph convolutional networks lessons learned"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "pykg2vec a python library for knowledge graph embedding"
    - "knowledge graph alignment network with gated multi hop neighborhood aggregation"
    - "a re evaluation of knowledge graph completion methods"
    - "hypernetwork knowledge graph embeddings"
    - "learning to exploit long term relational dependencies in knowledge graphs"

Connected through hyperedge HE-2 (induced by source paper "coke contextualized knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "hypernetwork knowledge graph embeddings"
    - "tucker tensor factorization for knowledge graph completion"
    - "probabilistic logic neural networks for reasoning"
    - "deep contextualized word representations"
    - "recurrent one hop predictions for reasoning over knowledge graphs"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "probabilistic logic neural networks for reasoning"):
  with members:
    - "reasoning visual dialogs with structural and partial observations"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "gmnn graph markov neural networks"
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "learning human object interactions by graph parsing neural networks"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "knowledge graph embedding with iterative guidance from soft rules"

Connected through hyperedge HE-4 (induced by source paper "iteratively learning embeddings and rules for knowledge graph reasoning"):
  with members:
    - "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-5 (induced by source paper "logicenn a neural based knowledge graphs embedding model with logical rules"):
  with members:
    - "quaternion knowledge graph embedding"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "on multi relational link prediction with bilinear models"
    - "adversarial sets for regularising neural link predictors"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "lifted rule injection for relation embeddings"

Connected through hyperedge HE-6 (induced by source paper "group representation theory for knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "quaternion knowledge graph embedding"
    - "can graph neural networks help logic reasoning"
    - "probabilistic logic neural networks for reasoning"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "toruse knowledge graph embedding on a lie group"
    - "chains of reasoning over entities relations and text using recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "scef a support confidence aware embedding framework for knowledge graph refinement"):
  with members:
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "does william shakespeare really write hamlet knowledge representation learning with confidence"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "proje embedding projection for knowledge graph completion"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"

Connected through hyperedge HE-8 (induced by source paper "on the knowledge graph completion using translation based embedding the loss is as important as the score"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "soft marginal transe for scholarly knowledge graph completion"
    - "improved knowledge graph embedding using background taxonomic information"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "on multi relational link prediction with bilinear models"
    - "toruse knowledge graph embedding on a lie group"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"

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)