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 "multilingual knowledge graph embeddings for cross lingual knowledge alignment"):
  Abstract: Many recent works have demonstrated the benefits of knowledge graph embeddings in completing monolingual knowledge graphs. Inasmuch as related knowledge bases are built in several different languages, achieving cross-lingual knowledge alignment will help people in constructing a coherent knowledge base, and assist machines in dealing with different expressions of entity relationships across diverse human languages. Unfortunately, achieving this highly desirable crosslingual alignment by human labor is very costly and errorprone. Thus, we propose MTransE, a translation-based model for multilingual knowledge graph embeddings, to provide a simple and automated solution. By encoding entities and relations of each language in a separated embedding space, MTransE provides transitions for each embedding vector to its cross-lingual counterparts in other spaces, while preserving the functionalities of monolingual embeddings. We deploy three different techniques to represent cross-lingual transitions, namely axis calibration, translation vectors, and linear transformations, and derive five variants for MTransE using different loss functions. Our models can be trained on partially aligned graphs, where just a small portion of triples are aligned with their cross-lingual counterparts. The experiments on cross-lingual entity matching and triple-wise alignment verification show promising results, with some variants consistently outperforming others on different tasks. We also explore how MTransE preserves the key properties of its monolingual counterpart TransE.

Includes members:
  Member "stranse a novel embedding model of entities and relationships in knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "locally adaptive translation for knowledge graph embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "kgvec2go knowledge graph embeddings as a service")
  Member "holographic embeddings of knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "knowledge graphs")
  Member "open question answering with weakly supervised embedding models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural graph embedding methods for natural language processing")
  Member "an autoencoder approach to learning bilingual word representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "connecting language and knowledge bases with embedding models for relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "a semantic matching energy function for learning with multi relational data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint embedding learning of educational knowledge graphs")
  Member "exact solutions to the nonlinear dynamics of learning in deep linear neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformation based adversarial video prediction on large scale data")

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.AI(Artificial Intelligence)