human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "leveraging structural and semantic correspondence for attribute oriented aspect sentiment discovery"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "generating sentences from disentangled syntactic and semantic spaces"
    - "disentangled representation learning for non parallel text style transfer"
    - "improving topic models with latent feature word representations"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "attention is all you need"
    - "distributed representations of words and phrases and their compositionality"
    - "the author topic model for authors and documents"

Connected through hyperedge HE-2 (induced by source paper "structured neural topic models for reviews"):
  with members:
    - "implicit reparameterization gradients"
    - "isolating sources of disentanglement in variational autoencoders"
    - "auto encoding total correlation explanation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "toward controlled generation of text"
    - "aspect augmented adversarial networks for domain adaptation"
    - "infovae information maximizing variational autoencoders"
    - "joint deep modeling of users and items using reviews for recommendation"

Question: Please tell me which class the center node 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)