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 "is aligning embedding spaces a challenging task an analysis of the existing methods"):
  Abstract: Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenarios have recently gained momentum. In order to make use of multiple KG embeddings for knowledge-driven applications such as question answering, named entity disambiguation, knowledge graph completion, etc., alignment of different KG embedding spaces is necessary. In addition to multilinguality and domain-specific information, different KGs pose the problem of structural differences making the alignment of the KG embeddings more challenging. This paper provides a theoretical analysis and comparison of the state-of-the-art alignment methods between two embedding spaces representing entity-entity and entity-word. This paper also aims at assessing the capability and short-comings of the existing alignment methods on the pretext of different applications.

Includes members:
  Member "joint representation learning of cross lingual words and entities via attentive distant supervision":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "kepler a unified model for knowledge embedding and pre trained language representation")
  Member "word translation without parallel data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep graph matching consensus")
  Member "joint representation learning of text and knowledge for knowledge graph completion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kepler a unified model for knowledge embedding and pre trained language representation")
  Member "multilingual knowledge graph embeddings for cross lingual knowledge alignment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "joint learning of the embedding of words and entities for named entity disambiguation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning cross context entity representations from text")
  Member "embedding entities and relations for learning and inference in knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge graphs")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "exploiting similarities among languages for machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)