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: "neural autoregressive collaborative filtering for implicit feedback"
  Abstract: This paper proposes implicit CF-NADE, a neural autoregressive model for collaborative filtering tasks using implicit feedback( e.g. click/watch/browse behaviors). We first convert a user's implicit feedback into a "like" vector and a confidence vector, and then model the probability of the "like" vector, weighted by the confidence vector. The training objective of implicit CF-NADE is to maximize a weighted negative log-likelihood. We test the performance of implicit CF-NADE on a dataset collected from a popular digital TV streaming service. More specifically, in the experiments, we describe how to convert watch counts into implicit "relative rating", and feed into implicit CF-NADE. Then we compare the performance of implicit CF-NADE model with the popular implicit matrix factorization approach. Experimental results show that implicit CF-NADE significantly outperforms the baseline.

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"
    - "collaborative filtering with user item co autoregressive models"
    - "a neural autoregressive approach to collaborative filtering"
    - "variational deep embedding an unsupervised and generative approach to clustering"

Connected through hyperedge HE-3 (induced by source paper "embarrassingly shallow autoencoders for sparse data"):
  with members:
    - "collaborative filtering via high dimensional regression"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "neural collaborative filtering"
    - "wide deep learning for recommender systems"
    - "a neural autoregressive approach to collaborative filtering"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "on hybrid modular recommendation systems for video streaming"):
  with members:
    - "hybrid recommender system based on autoencoders"
    - "matrix completion with queries"
    - "feature weighted linear stacking"

Connected through hyperedge HE-5 (induced by source paper "spectral collaborative filtering"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "mars memory attention aware recommender system"
    - "neural collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-6 (induced by source paper "cross domain recommendation via deep domain adaptation"):
  with members:
    - "neural collaborative filtering"
    - "learning to remember rare events"
    - "a neural autoregressive approach to collaborative filtering"
    - "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
    - "domain separation networks"
    - "lshtc a benchmark for large scale text classification"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-7 (induced by source paper "neural collaborative filtering"):
  with members:
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "wide deep learning for recommender systems"
    - "a neural autoregressive approach to collaborative filtering"
    - "a generic coordinate descent framework for learning from implicit feedback"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "collaborative deep learning for recommender systems"
    - "bpr bayesian personalized ranking from implicit feedback"

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.IR(Information Retrieval)