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: "product based neural networks for user response prediction"
  Abstract: Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising. The data in those applications is mostly categorical and contains multiple fields; a typical representation is to transform it into a high-dimensional sparse binary feature representation via one-hot encoding. Facing with the extreme sparsity, traditional models may limit their capacity of mining shallow patterns from the data, i.e. low-order feature combinations. Deep models like deep neural networks, on the other hand, cannot be directly applied for the high-dimensional input because of the huge feature space. In this paper, we propose a Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between inter-field categories, and further fully connected layers to explore high-order feature interactions. Our experimental results on two large-scale real-world ad click datasets demonstrate that PNNs consistently outperform the state-of-the-art models on various metrics.

Connected through hyperedge HE-1 (induced by source paper "autoemb automated embedding dimensionality search in streaming recommendations"):
  with members:
    - "adabert task adaptive bert compression with differentiable neural architecture search"
    - "mixed dimension embeddings with application to memory efficient recommendation systems"
    - "neural input search for large scale recommendation models"
    - "deep learning based recommender system a survey and new perspectives"
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"

Connected through hyperedge HE-2 (induced by source paper "metaselector meta learning for recommendation with user level adaptive model selection"):
  with members:
    - "a closer look at few shot classification"
    - "neural graph collaborative filtering"
    - "meta learning pac bayes priors in model averaging"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "one at a time a meta learning recommender system for recommendation algorithm selection on micro level"
    - "federated meta learning with fast convergence and efficient communication"
    - "neural factorization machines for sparse predictive analytics"

Connected through hyperedge HE-3 (induced by source paper "a sparse deep factorization machine for efficient ctr prediction"):
  with members:
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "importance estimation for neural network pruning"
    - "structured pruning of neural networks with budget aware regularization"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "field weighted factorization machines for click through rate prediction in display advertising"
    - "rethinking the value of network pruning"
    - "neural factorization machines for sparse predictive analytics"

Connected through hyperedge HE-4 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling"):
  with members:
    - "lifelong sequential modeling with personalized memorization for user response prediction"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "session based social recommendation via dynamic graph attention networks"
    - "deep learning based recommender system a survey and new perspectives"
    - "outer product based neural collaborative filtering"
    - "repeatnet a repeat aware neural recommendation machine for session based recommendation"
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "recurrent neural networks for long and short term sequential recommendation"

Connected through hyperedge HE-5 (induced by source paper "distributed hierarchical gpu parameter server for massive scale deep learning ads systems"):
  with members:
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "bandana using non volatile memory for storing deep learning models"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "the case for learned index structures"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"

Connected through hyperedge HE-6 (induced by source paper "deep time stream framework for click through rate prediction by tracking interest evolution"):
  with members:
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "wide deep learning for recommender systems"
    - "image based recommendations on styles and substitutes"

Connected through hyperedge HE-7 (induced by source paper "lifelong sequential modeling with personalized memorization for user response prediction"):
  with members:
    - "on training recurrent neural networks for lifelong learning"
    - "collaborative memory network for recommendation systems"
    - "learning multi touch conversion attribution with dual attention mechanisms for online advertising"
    - "next item recommendation with self attention"
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "recurrent neural networks for long and short term sequential recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"

Connected through hyperedge HE-8 (induced by source paper "fi gnn modeling feature interactions via graph neural networks for ctr prediction"):
  with members:
    - "session based recommendation with graph neural network"
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "semi supervised compatibility learning across categories for clothing matching"
    - "dressing as a whole outfit compatibility learning based on node wise graph neural networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "graph to sequence learning using gated graph neural networks"

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)