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: "autoint automatic feature interaction learning via self attentive neural networks"
  Abstract: Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad or an item, is critical to many online applications such as online advertising and recommender systems. The problem is very challenging since (1) the input features (e.g., the user id, user age, item id, item category) are usually sparse and high-dimensional, and (2) an effective prediction relies on high-order combinatorial features (a.k.a. cross features), which are very time-consuming to hand-craft by domain experts and are impossible to be enumerated. Therefore, there have been efforts in finding low-dimensional representations of the sparse and high-dimensional raw features and their meaningful combinations. In this paper, we propose an effective and efficient method called the AutoInt to automatically learn the high-order feature interactions of input features. Our proposed algorithm is very general, which can be applied to both numerical and categorical input features. Specifically, we map both the numerical and categorical features into the same low-dimensional space. Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space. With different layers of the multi-head self-attentive neural networks, different orders of feature combinations of input features can be modeled. The whole model can be efficiently fit on large-scale raw data in an end-to-end fashion. Experimental results on four real-world datasets show that our proposed approach not only outperforms existing state-of-the-art approaches for prediction but also offers good explainability. Code is available at: \urlhttps://github.com/DeepGraphLearning/RecommenderSystems.

Connected through hyperedge HE-1 (induced by source paper "a sparse deep factorization machine for efficient ctr prediction"):
  with members:
    - "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"
    - "deep cross network for ad click predictions"

Connected through hyperedge HE-2 (induced by source paper "safe scalable automatic feature engineering framework for industrial tasks"):
  with members:
    - "interpretable mtl from heterogeneous domains using boosted tree"
    - "automl a survey of the state of the art"
    - "survey on automated machine learning"
    - "taking human out of learning applications a survey on automated machine learning"
    - "feature engineering for predictive modeling using reinforcement learning"
    - "optimized cost per click in taobao display advertising"
    - "deep cross network for ad click predictions"
    - "one button machine for automating feature engineering in relational databases"

Connected through hyperedge HE-3 (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"
    - "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"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"

Connected through hyperedge HE-4 (induced by source paper "learning feature interactions with lorentzian factorization machine"):
  with members:
    - "scalable hyperbolic recommender systems"
    - "hyperbolic attention networks"
    - "riemannian adaptive optimization methods"
    - "hyperbolic recommender systems"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"

Connected through hyperedge HE-5 (induced by source paper "adaptive factorization network learning adaptive order feature interactions"):
  with members:
    - "product based neural networks for user response prediction over multi field categorical data"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "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"
    - "cross device user matching based on massive browse logs the runner up solution for the 2016 cikm cup"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-6 (induced by source paper "neural hybrid recommender recommendation needs collaboration"):
  with members:
    - "neural collaborative filtering"
    - "deep cross network for ad click predictions"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "wide deep learning for recommender systems"
    - "a generic coordinate descent framework for learning from implicit feedback"
    - "neural network matrix factorization"

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)