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 "leveraging structural and semantic correspondence for attribute oriented aspect sentiment discovery"):
  Abstract: Opinionated text often involves attributes such as authorship and location that influence the sentiments expressed for different aspects. We posit that structural and semantic correspondence is both prevalent in opinionated text, especially when associated with attributes, and crucial in accurately revealing its latent aspect and sentiment structure. However, it is not recognized by existing approaches. #R##N#We propose Trait, an unsupervised probabilistic model that discovers aspects and sentiments from text and associates them with different attributes. To this end, Trait infers and leverages structural and semantic correspondence using a Markov Random Field. We show empirically that by incorporating attributes explicitly Trait significantly outperforms state-of-the-art baselines both by generating attribute profiles that accord with our intuitions, as shown via visualization, and yielding topics of greater semantic cohesion.

Includes members:
  Member "a multi task approach for disentangling syntax and semantics in sentence representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "controllable paraphrase generation with a syntactic exemplar")
  Member "generating sentences from disentangled syntactic and semantic spaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "variational template machine for data to text generation")
  Member "disentangled representation learning for non parallel text style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "improving topic models with latent feature word representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "learning disentangled representations of texts with application to biomedical abstracts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "structured neural topic models for reviews")
  Member "evaluation of sentence embeddings in downstream and linguistic probing tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "distributed representations of words and phrases and their compositionality":
    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)