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 "learning disentangled representations of texts with application to biomedical abstracts"):
  Abstract: We propose a method for learning disentangled sets of vector representations of texts that capture distinct aspects. We argue that such representations afford model transfer and interpretability. To induce disentangled embeddings, we propose an adversarial objective based on the (dis)similarity between triplets of documents w.r.t. specific aspects. Our motivating application concerns embedding abstracts describing clinical trials in a manner that disentangles the populations, interventions, and outcomes in a given trial. We show that the induced representations indeed encode these targeted clinically salient aspects and that they can be effectively used to perform aspect-specific retrieval. We demonstrate that the approach generalizes beyond this motivating example via experiments on two multi-aspect review corpora.

Includes members:
  Member "aspect augmented adversarial networks for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised adversarial domain adaptation for implicit discourse relation classification")
  Member "disentangled representations for manipulation of sentiment in text":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "music style transfer a position paper")
  Member "a hierarchical model of reviews for aspect based sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a novel aspect guided deep transition model for aspect based sentiment analysis")
  Member "conditional similarity networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network")
  Member "disentangled representations in neural models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pair wise exchangeable feature extraction for arbitrary style transfer")
  Member "infogan interpretable representation learning by information maximizing generative adversarial nets":
    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 "neural variational inference for text processing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")

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)