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: "hybrid collaborative filtering with autoencoders"
  Abstract: Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendations. Such algorithms look for latent variables in a large sparse matrix of ratings. They can be enhanced by adding side information to tackle the well-known cold start problem. While Neu-ral Networks have tremendous success in image and speech recognition, they have received less attention in Collaborative Filtering. This is all the more surprising that Neural Networks are able to discover latent variables in large and heterogeneous datasets. In this paper, we introduce a Collaborative Filtering Neural network architecture aka CFN which computes a non-linear Matrix Factorization from sparse rating inputs and side information. We show experimentally on the MovieLens and Douban dataset that CFN outper-forms the state of the art and benefits from side information. We provide an implementation of the algorithm as a reusable plugin for Torch, a popular Neural Network framework.

Connected through hyperedge HE-1 (induced by source paper "regularizing autoencoder based matrix completion models via manifold learning"):
  with members:
    - "twitter user geolocation using deep multiview learning"
    - "deep learning sparse ternary projections for compressed sensing of images"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "hybrid collaborative filtering with neural networks"
    - "a neural autoregressive approach to collaborative filtering"
    - "hybrid recommender system based on autoencoders"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "learning to make predictions on graphs with autoencoders"):
  with members:
    - "focal loss for dense object detection"
    - "representation learning on graphs methods and applications"
    - "semi supervised classification with graph convolutional networks"
    - "trace norm regularised deep multi task learning"
    - "revisiting semi supervised learning with graph embeddings"
    - "a fully convolutional neural network for cardiac segmentation in short axis mri"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "collaborative autoencoder for recommender systems"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "wide deep learning for recommender systems"
    - "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "collaborative deep learning for recommender systems"

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)