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: "soft marginal transe for scholarly knowledge graph completion"
  Abstract: Knowledge graphs (KGs), i.e. representation of information as a semantic graph, provide a significant test bed for many tasks including question answering, recommendation, and link prediction. Various amount of scholarly metadata have been made vailable as knowledge graphs from the diversity of data providers and agents. However, these high-quantities of data remain far from quality criteria in terms of completeness while growing at a rapid pace. Most of the attempts in completing such KGs are following traditional data digitization, harvesting and collaborative curation approaches. Whereas, advanced AI-related approaches such as embedding models - specifically designed for such tasks - are usually evaluated for standard benchmarks such as Freebase and Wordnet. The tailored nature of such datasets prevents those approaches to shed the lights on more accurate discoveries. Application of such models on domain-specific KGs takes advantage of enriched meta-data and provides accurate results where the underlying domain can enormously benefit. In this work, the TransE embedding model is reconciled for a specific link prediction task on scholarly metadata. The results show a significant shift in the accuracy and performance evaluation of the model on a dataset with scholarly metadata. The newly proposed version of TransE obtains 99.9% for link prediction task while original TransE gets 95%. In terms of accuracy and Hit@10, TransE outperforms other embedding models such as ComplEx, TransH and TransR experimented over scholarly knowledge graphs

Connected through hyperedge HE-1 (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"
    - "improved knowledge graph embedding using background taxonomic information"
    - "improving knowledge graph embedding using simple constraints"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "toward understanding the effect of loss function on then performance of knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "improved knowledge graph embedding using background taxonomic information"
    - "improving knowledge graph embedding using simple constraints"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "toruse knowledge graph embedding on a lie group"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"

Connected through hyperedge HE-3 (induced by source paper "adaptive margin ranking loss for knowledge graph embeddings via a correntropy objective function"):
  with members:
    - "complex embeddings for simple link prediction"
    - "question answering with subgraph 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)