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 "assessing the lexico semantic relational knowledge captured by word and concept embeddings"):
  Abstract: Deep learning currently dominates the benchmarks for various NLP tasks and, at the basis of such systems, words are frequently represented as embeddings --vectors in a low dimensional space-- learned from large text corpora and various algorithms have been proposed to learn both word and concept embeddings. One of the claimed benefits of such embeddings is that they capture knowledge about semantic relations. Such embeddings are most often evaluated through tasks such as predicting human-rated similarity and analogy which only test a few, often ill-defined, relations. In this paper, we propose a method for (i) reliably generating word and concept pair datasets for a wide number of relations by using a knowledge graph and (ii) evaluating to what extent pre-trained embeddings capture those relations. We evaluate the approach against a proprietary and a public knowledge graph and analyze the results, showing which lexico-semantic relational knowledge is captured by current embedding learning approaches.

Includes members:
  Member "not just about size a study on the role of distributed word representations in the analysis of scientific publications":
    also connected through other hyperedges:
      (none)
  Member "a decomposable attention model for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "swivel improving embeddings by noticing what s missing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form")
  Member "enriching word vectors with subword information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "path based vs distributional information in recognizing lexical semantic relations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification")
  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 "visualizing and understanding neural models in nlp":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "visual summary of value level feature attribution in prediction classes with recurrent neural networks")
  Member "experiments with three approaches to recognizing lexical entailment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning multi sense word distributions using approximate kullback leibler divergence")

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)