human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "neighborhood mixture model for knowledge base completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relational memory based knowledge graph embedding")
  Member "ssp semantic space projection for knowledge graph embedding with text descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "can x2vec save lives integrating graph and language embeddings for automatic mental health classification")
  Member "discriminative gaifman models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relational neural machines")
  Member "chains of reasoning over entities relations and text using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "query2box reasoning over knowledge graphs in vector space using box embeddings")
  Member "proje embedding projection for knowledge graph completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "complex embeddings for simple link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "type constrained representation learning in knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on knowledge graph embeddings with literals which model links better literal ly")
  Member "modeling relation paths for representation learning of knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")

Question: Please tell me which class the source paper 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)