human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "irreflexive and hierarchical relations as translations"
  Abstract: We consider the problem of embedding entities and relations of knowledge bases in lowdimensional vector spaces. Unlike most existing approaches, which are primarily efficient for modeling equivalence relations, our approach is designed to explicitly model irreflexive relations, such as hierarchies, by interpreting them as translations operating on the low-dimensional embeddings of the entities. Preliminary experiments show that, despite its simplicity and a smaller number of parameters than previous approaches, our approach achieves state-of-the-art performance according to standard evaluation protocols on data from WordNet and Freebase.

Connected through hyperedge HE-1 (induced by source paper "on inductive abilities of latent factor models for relational learning"):
  with members:
    - "knowledge base completion with out of knowledge base entities a graph neural network approach"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "knowledge graph completion via complex tensor factorization"
    - "complex and holographic embeddings of knowledge graphs a comparison"
    - "adversarial sets for regularising neural link predictors"
    - "generalizing to unseen entities and entity pairs with row less universal schema"
    - "basic reasoning with tensor product representations"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-2 (induced by source paper "complex embeddings for simple link prediction"):
  with members:
    - "a factorization machine framework for testing bigram embeddings in knowledgebase completion"
    - "holographic embeddings of knowledge graphs"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-3 (induced by source paper "large margin nearest neighbor embedding for knowledge representation"):
  with members:
    - "distributed representations of sentences and documents"
    - "a semantic matching energy function for learning with multi relational data"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "connecting language and knowledge bases with embedding models for relation extraction"):
  with members:
    - "a joint model of language and perception for grounded attribute learning"

Question: Please tell me which class the center node 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.LG(Machine Learning)