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 "neighborhood mixture model for knowledge base completion"):
  Abstract: Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete. In this paper, we define a novel entity representation as a mixture of its neighborhood in the knowledge base and apply this technique on TransE-a well-known embedding model for knowledge base completion. Experimental results show that the neighborhood information significantly helps to improve the results of the TransE model, leading to better performance than obtained by other state-of-the-art embedding models on three benchmark datasets for triple classification, entity prediction and relation prediction tasks.

Includes members:
  Member "discriminative gaifman models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "query2box reasoning over knowledge graphs in vector space using box embeddings")
  Member "stranse a novel embedding model of entities and relationships in knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "proje embedding projection for knowledge graph completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (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-7 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "from one point to a manifold knowledge graph embedding for precise link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an evaluation of knowledge graph embeddings for autonomous driving data experience and practice")

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)