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 learning over multi field categorical data a case study on user response prediction"
  Abstract: Predicting user responses, such as click-through rate and conversion rate, are critical in many web applications including web search, personalised recommendation, and online advertising. Different from continuous raw features that we usually found in the image and audio domains, the input features in web space are always of multi-field and are mostly discrete and categorical while their dependencies are little known. Major user response prediction models have to either limit themselves to linear models or require manually building up high-order combination features. The former loses the ability of exploring feature interactions, while the latter results in a heavy computation in the large feature space. To tackle the issue, we propose two novel models using deep neural networks (DNNs) to automatically learn effective patterns from categorical feature interactions and make predictions of users' ad clicks. To get our DNNs efficiently work, we propose to leverage three feature transformation methods, i.e., factorisation machines (FMs), restricted Boltzmann machines (RBMs) and denoising auto-encoders (DAEs). This paper presents the structure of our models and their efficient training algorithms. The large-scale experiments with real-world data demonstrate that our methods work better than major state-of-the-art models.

Connected through hyperedge HE-1 (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-2 (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"

Connected through hyperedge HE-3 (induced by source paper "feature generation by convolutional neural network for click through rate prediction"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "deepfm an end to end wide deep learning framework for ctr prediction"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "learning to generate reviews and discovering sentiment"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "failures of gradient based deep learning"

Connected through hyperedge HE-4 (induced by source paper "learning to recommend via meta parameter partition"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "online meta learning"
    - "on first order meta learning algorithms"
    - "deep reinforcement learning for page wise recommendations"
    - "generative adversarial user model for reinforcement learning based recommendation system"
    - "federated meta learning for recommendation"
    - "federated meta learning with fast convergence and efficient communication"
    - "deep reinforcement learning for list wise recommendations"

Connected through hyperedge HE-5 (induced by source paper "distributed equivalent substitution training for large scale recommender systems"):
  with members:
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "integrated model batch and domain parallelism in training neural networks"
    - "device placement optimization with reinforcement learning"
    - "revisiting distributed synchronous sgd"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-6 (induced by source paper "an enhanced ad event prediction method based on feature engineering"):
  with members:
    - "click through rate prediction for contextual advertisment using linear regression"
    - "wide deep learning for recommender systems"
    - "deep ctr prediction in display advertising"
    - "a review of feature selection methods based on mutual information"
    - "sequential click prediction for sponsored search with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "product based neural networks for user response prediction over multi field categorical data"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "deep sets"
    - "self normalizing neural networks"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "deepmood modeling mobile phone typing dynamics for mood detection"
    - "failures of gradient based deep learning"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning based recommendation with explicit user item interactions modeling"):
  with members:
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "deep reinforcement learning for list wise recommendations"
    - "efficient architecture search by network transformation"
    - "real time bidding by reinforcement learning in display advertising"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "wide deep learning for recommender systems"
    - "neural architecture search with reinforcement learning"

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)