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 "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"):
  Abstract: Despite the success achieved on various natural language processing tasks, word embeddings are difficult to interpret due to the dense vector representations. This paper focuses on interpreting the embeddings for various aspects, including sense separation in the vector dimensions and definition generation. Specifically, given a context together with a target word, our algorithm first projects the target word embedding to a high-dimensional sparse vector and picks the specific dimensions that can best explain the semantic meaning of the target word by the encoded contextual information, where the sense of the target word can be indirectly inferred. Finally, our algorithm applies an RNN to generate the textual definition of the target word in the human readable form, which enables direct interpretation of the corresponding word embedding. This paper also introduces a large and high-quality context-definition dataset that consists of sense definitions together with multiple example sentences per polysemous word, which is a valuable resource for definition modeling and word sense disambiguation. The conducted experiments show the superior performance in BLEU score and the human evaluation test.

Includes members:
  Member "learning to compute word embeddings on the fly":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving pre trained multilingual models with vocabulary expansion")
  Member "spine sparse interpretable neural embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "word translation without parallel data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep graph matching consensus")
  Member "muse modularizing unsupervised sense embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross topic distributional semantic representations via unsupervised mappings")
  Member "linear algebraic structure of word senses with applications to polysemy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "the mythos of model interpretability":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "skip thought vectors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "learning to understand phrases by embedding the dictionary":
    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)