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 "neural latent relational analysis to capture lexical semantic relations in a vector space"):
  Abstract: Capturing the semantic relations of words in a vector space contributes to many natural language processing tasks. One promising approach exploits lexico-syntactic patterns as features of word pairs. In this paper, we propose a novel model of this pattern-based approach, neural latent relational analysis (NLRA). NLRA can generalize co-occurrences of word pairs and lexico-syntactic patterns, and obtain embeddings of the word pairs that do not co-occur. This overcomes the critical data sparseness problem encountered in previous pattern-based models. Our experimental results on measuring relational similarity demonstrate that NLRA outperforms the previous pattern-based models. In addition, when combined with a vector offset model, NLRA achieves a performance comparable to that of the state-of-the-art model that exploits additional semantic relational data.

Includes members:
  Member "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification")
  Member "analogical inference for multi relational embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "generalizing to unseen entities and entity pairs with row less universal schema":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentiable reasoning on large knowledge bases and natural language")
  Member "improving hypernymy detection with an integrated path based and distributional method":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification")
  Member "complex embeddings for simple link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "holographic embeddings of knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "knowledge graphs")
  Member "embedding semantic relations into word representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an interpretable knowledge transfer model for knowledge base completion")

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)