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 "compositional learning of relation paths embedding for knowledge base completion"):
  Abstract: Nowadays, large-scale knowledge bases containing billions of facts have reached impressive sizes; however, they are still far from completion. In addition, most existing methods only consider the direct links between entities, ignoring the vital impact about the semantic of relation paths. In this paper, we study the problem of how to better embed entities and relations into different low dimensional spaces. A compositional learning model of relation paths embedding (RPE) is proposed to take full advantage of additional semantic information expressed by relation paths. More specifically, using corresponding projection matrices, RPE can simultaneously embed entities into corresponding relation and path spaces. It is also suggested that type constraints could be extended from traditional relation-specific to the new proposed path-specific ones. Both of the two type constraints can be seamlessly incorporated into RPE and decrease the errors in prediction. Experiments are conducted on the benchmark datasets and the proposed model achieves significant and consistent improvements compared with the state-of-the-art algorithms for knowledge base completion.

Includes members:
  Member "type constrained representation learning in knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on knowledge graph embeddings with literals which model links better literal ly")
  Member "traversing knowledge graphs in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "compositional vector space models for knowledge base completion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vqa lol visual question answering under the lens of logic")

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)