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: "document neural autoregressive distribution estimation"
  Abstract: We present an approach based on feed-forward neural networks for learning the distribution of textual documents. This approach is inspired by the Neural Autoregressive Distribution Estimator(NADE) model, which has been shown to be a good estimator of the distribution of discrete-valued igh-dimensional vectors. In this paper, we present how NADE can successfully be adapted to the case of textual data, retaining from NADE the property that sampling or computing the probability of observations can be done exactly and efficiently. The approach can also be used to learn deep representations of documents that are competitive to those learned by the alternative topic modeling approaches. Finally, we describe how the approach can be combined with a regular neural network N-gram model and substantially improve its performance, by making its learned representation sensitive to the larger, document-specific context.

Connected through hyperedge HE-1 (induced by source paper "nat neural architecture transformer for accurate and compact architectures"):
  with members:
    - "multi marginal wasserstein gan"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "the evolved transformer"
    - "learnable embedding space for efficient neural architecture compression"
    - "graph convolutional networks for temporal action localization"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "adversarial learning with local coordinate coding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "rafm rank aware factorization machines"):
  with members:
    - "a boosting framework of factorization machine"
    - "neural factorization machines for sparse predictive analytics"
    - "deep cross network for ad click predictions"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "neural autoregressive collaborative filtering for implicit feedback"
    - "collaborative filtering with user item co autoregressive models"
    - "a neural autoregressive approach to collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "lifelong neural topic learning in contextualized autoregressive topic models of language via informative transfers"):
  with members:
    - "improving topic models with latent feature word representations"
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"
    - "variational inference in pachinko allocation machines"
    - "replicated siamese lstm in ticketing system for similarity learning and retrieval in asymmetric texts"
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "distributed representations of sentences and documents"
    - "probabilistic latent semantic analysis"

Connected through hyperedge HE-4 (induced by source paper "multi view and multi source transfers in neural topic modeling"):
  with members:
    - "improving topic models with latent feature word representations"
    - "deep contextualized word representations"
    - "neural variational inference for text processing"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-5 (induced by source paper "multi view and multi source transfers in neural topic modeling with pretrained topic and word embeddings"):
  with members:
    - "neural variational inference for text processing"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "explaining away syntactic structure in semantic document representations"):
  with members:
    - "vae learning via stein variational gradient descent"
    - "modeling documents with generative adversarial networks"
    - "piecewise latent variables for neural variational text processing"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "generating sentences from a continuous space"
    - "neural variational inference for text processing"
    - "document embedding with paragraph vectors"

Connected through hyperedge HE-7 (induced by source paper "texttovec deep contextualized neural autoregressive topic models of language with distributed compositional prior"):
  with members:
    - "improving topic models with latent feature word representations"
    - "deep contextualized word representations"
    - "deep temporal recurrent replicated softmax for topical trends over time"
    - "topically driven neural language model"
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "document informed neural autoregressive topic models with distributional prior"):
  with members:
    - "improving topic models with latent feature word representations"
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "combining recurrent and convolutional neural networks for relation classification"
    - "distributed representations of sentences and documents"

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.LG(Machine Learning)