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: "deep cross network for ad click predictions"
  Abstract: Feature engineering has been the key to the success of many prediction models. However, the process is non-trivial and often requires manual feature engineering or exhaustive searching. DNNs are able to automatically learn feature interactions; however, they generate all the interactions implicitly, and are not necessarily efficient in learning all types of cross features. In this paper, we propose the Deep & Cross Network (DCN) which keeps the benefits of a DNN model, and beyond that, it introduces a novel cross network that is more efficient in learning certain bounded-degree feature interactions. In particular, DCN explicitly applies feature crossing at each layer, requires no manual feature engineering, and adds negligible extra complexity to the DNN model. Our experimental results have demonstrated its superiority over the state-of-art algorithms on the CTR prediction dataset and dense classification dataset, in terms of both model accuracy and memory usage.

Connected through hyperedge HE-1 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "graph representation learning via hard and channel wise attention networks"
    - "towards better text understanding and retrieval through kernel entity salience modeling"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hypergraph neural networks"
    - "graph convolutional networks for text classification"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "deepinf social influence prediction with deep learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "subclass distillation"):
  with members:
    - "relational knowledge distillation"
    - "momentum contrast for unsupervised visual representation learning"
    - "variational information distillation for knowledge transfer"
    - "contrastive representation distillation"
    - "patient knowledge distillation for bert model compression"
    - "similarity preserving knowledge distillation"
    - "self training with noisy student improves imagenet classification"
    - "paraphrasing complex network network compression via factor transfer"

Connected through hyperedge HE-4 (induced by source paper "generalized embedding machines for recommender systems"):
  with members:
    - "a sufficient condition for convergences of adam and rmsprop"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "strongly hierarchical factorization machines and anova kernel regression"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "multi view machines"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "safe scalable automatic feature engineering framework for industrial tasks"):
  with members:
    - "interpretable mtl from heterogeneous domains using boosted tree"
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "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"
    - "one button machine for automating feature engineering in relational databases"

Connected through hyperedge HE-6 (induced by source paper "scalable bid landscape forecasting in real time bidding"):
  with members:
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "machine learning for survival analysis a survey"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "adam a method for stochastic optimization"
    - "real time bidding benchmarking with ipinyou dataset"
    - "real time bidding for online advertising measurement and analysis"

Connected through hyperedge HE-7 (induced by source paper "developing a recommendation benchmark for mlperf training and inference"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "scalable realistic recommendation datasets through fractal expansions"
    - "mlperf inference benchmark"

Connected through hyperedge HE-8 (induced by source paper "the architectural implications of facebook s dnn based personalized recommendation"):
  with members:
    - "on the dimensionality of embeddings for sparse features and data"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "mlperf training benchmark"
    - "recnmp accelerating personalized recommendation with near memory processing"
    - "bandana using non volatile memory for storing deep learning models"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "tbd benchmarking and analyzing deep neural network training"

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)