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 "domain representation for knowledge graph embedding"):
  Abstract: Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning. However, most existing models ignore to represent hierarchical knowledge, such as the similarities and dissimilarities of entities in one domain. We proposed to learn a Domain Representations over existing knowledge graph embedding models, such that entities that have similar attributes are organized into the same domain. Such hierarchical knowledge of domains can give further evidence in link prediction. Experimental results show that domain embeddings give a significant improvement over the most recent state-of-art baseline knowledge graph embedding models.

Includes members:
  Member "response ranking with deep matching networks and external knowledge in information seeking conversation systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")
  Member "an interpretable knowledge transfer model for knowledge base completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hyperkg hyperbolic knowledge graph embeddings for knowledge base completion")
  Member "ssp semantic space projection for knowledge graph embedding with text descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "can x2vec save lives integrating graph and language embeddings for automatic mental health classification")
  Member "stranse a novel embedding model of entities and relationships in knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "modeling relation paths for representation learning of knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "kgvec2go knowledge graph embeddings as a service")
  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.AI(Artificial Intelligence)