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: "automatic aggregation by joint modeling of aspects and values"
  Abstract: We present a model for aggregation of product review snippets by joint aspect identification and sentiment analysis. Our model simultaneously identifies an underlying set of ratable aspects presented in the reviews of a product (e.g., sushi and miso for a Japanese restaurant) and determines the corresponding sentiment of each aspect. This approach directly enables discovery of highly-rated or inconsistent aspects of a product. Our generative model admits an efficient variational mean-field inference algorithm. It is also easily extensible, and we describe several modifications and their effects on model structure and inference. We test our model on two tasks, joint aspect identification and sentiment analysis on a set of Yelp reviews and aspect identification alone on a set of medical summaries. We evaluate the performance of the model on aspect identification, sentiment analysis, and per-word labeling accuracy. We demonstrate that our model outperforms applicable baselines by a considerable margin, yielding up to 32% relative error reduction on aspect identification and up to 20% relative error reduction on sentiment analysis.

Connected through hyperedge HE-1 (induced by source paper "structured neural topic models for reviews"):
  with members:
    - "implicit reparameterization gradients"
    - "learning disentangled representations of texts with application to biomedical abstracts"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "learning disentangled representations of texts with application to biomedical abstracts"):
  with members:
    - "aspect augmented adversarial networks for domain adaptation"
    - "disentangled representations for manipulation of sentiment in text"
    - "a hierarchical model of reviews for aspect based sentiment analysis"
    - "conditional similarity networks"
    - "disentangled representations in neural models"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "skip thought vectors"
    - "neural variational inference for text processing"

Connected through hyperedge HE-3 (induced by source paper "deep memory networks for attitude identification"):
  with members:
    - "aspect level sentiment classification with deep memory network"
    - "stance and sentiment in tweets"
    - "end to end memory networks"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "identifying users with opposing opinions in twitter debates"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "aspect based opinion summarization with convolutional neural networks"):
  with members:
    - "classifying relations by ranking with convolutional neural networks"
    - "going deeper with convolutions"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"
    - "modeling online reviews with multi grain topic models"
    - "thumbs up sentiment classification using machine learning techniques"

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)