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 "hyperkg hyperbolic knowledge graph embeddings for knowledge base completion"):
  Abstract: Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns not captured in the original data. In this paper, we propose a novel embedding model, dubbed HyperKG, for knowledge base completion. Our model extends translational models by exploiting hyperbolic space in order to better reflect the topological properties of knowledge bases. We investigate the type of regularities that our model can capture and we show that HyperKG is a prominent candidate for effectively representing a subset of Datalog rules. We empirically show, using a variety of link prediction datasets, that HyperKG overcomes the performance barriers of Euclidean translational models narrowing the performance gap against the bilinear models.

Includes members:
  Member "rotate knowledge graph embedding by relational rotation in complex space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "tucker tensor factorization for knowledge graph completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "inferring concept hierarchies from text corpora via hyperbolic embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification")
  Member "multi relational poincar e graph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk")
  Member "learning continuous hierarchies in the lorentz model of hyperbolic geometry":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "poincar e glove hyperbolic word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "binarized pmi matrix bridging word embeddings and hyperbolic spaces")
  Member "hyperbolic neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "data augmentation with mobius transformations")
  Member "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "iteratively learning embeddings and rules for knowledge graph reasoning")

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)