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: "stranse a novel embedding model of entities and relationships in knowledge bases"
  Abstract: Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a lowdimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task.

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"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "on evaluating embedding models for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "hyperkg hyperbolic knowledge graph embeddings for knowledge base completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "multi relational poincar e graph embeddings"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "hyperbolic neural networks"
    - "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules"

Connected through hyperedge HE-3 (induced by source paper "diachronic embedding for temporal knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "hypernetwork knowledge graph embeddings"
    - "tucker tensor factorization for knowledge graph completion"
    - "improved knowledge graph embedding using background taxonomic information"
    - "how powerful are graph neural networks"
    - "learning dynamic embeddings from temporal interactions"
    - "learning sequence encoders for temporal knowledge graph completion"

Connected through hyperedge HE-4 (induced by source paper "bayes embedding bem refining representation by integrating knowledge graphs and behavior specific networks"):
  with members:
    - "graph networks as learnable physics engines for inference and control"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an attention based collaboration framework for multi view network representation learning"
    - "billion scale similarity search with gpus"
    - "a survey on network embedding"
    - "learning combinatorial optimization algorithms over graphs"
    - "a comprehensive survey of graph embedding problems techniques and applications"

Connected through hyperedge HE-5 (induced by source paper "tucker tensor factorization for knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "hypernetwork knowledge graph embeddings"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "toruse knowledge graph embedding on a lie group"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "analogical inference for multi relational embeddings"

Connected through hyperedge HE-6 (induced by source paper "investigating robustness and interpretability of link prediction via adversarial modifications"):
  with members:
    - "understanding the origins of bias in word embeddings"
    - "adversarial attack on graph structured data"
    - "graph embedding techniques applications and performance a survey"
    - "embedding multimodal relational data for knowledge base completion"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "answering visual relational queries in web extracted knowledge graphs"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "towards interpretable deep neural networks by leveraging adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "rotate knowledge graph embedding by relational rotation in complex space"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "end to end differentiable proving"
    - "knowledge base completion baselines strike back"
    - "convolutional 2d knowledge graph embeddings"
    - "chains of reasoning over entities relations and text using recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "multi relational poincar e graph embeddings"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "riemannian adaptive optimization methods"
    - "hyperbolic neural networks"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "differentiable learning of logical rules for knowledge base reasoning"

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)