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: "a generic coordinate descent framework for learning from implicit feedback"
  Abstract: In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide variety of applications. Despite this, learning from implicit feedback is still computationally challenging. So far, most work relies on stochastic gradient descent (SGD) solvers which are easy to derive, but in practice challenging to apply, especially for tasks with many items. For the simple matrix factorization model, an efficient coordinate descent (CD) solver has been previously proposed. However, efficient CD approaches have not been derived for more complex models. #R##N#In this paper, we provide a new framework for deriving efficient CD algorithms for complex recommender models. We identify and introduce the property of k-separable models. We show that k-separability is a sufficient property to allow efficient optimization of implicit recommender problems with CD. We illustrate this framework on a variety of state-of-the-art models including factorization machines and Tucker decomposition. To summarize, our work provides the theory and building blocks to derive efficient implicit CD algorithms for complex recommender models.

Connected through hyperedge HE-1 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems"):
  with members:
    - "deep conversational recommender in travel"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "toward building conversational recommender systems a contextual bandit approach"
    - "towards deep conversational recommendations"
    - "learning contextual bandits in a non stationary environment"
    - "collaborative memory network for recommendation systems"
    - "explicit state tracking with semi supervisionfor neural dialogue generation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"

Connected through hyperedge HE-2 (induced by source paper "fast adaptively weighted matrix factorization for recommendation with implicit feedback"):
  with members:
    - "adversarial sampling and training for semi supervised information retrieval"
    - "collaborative filtering with social exposure a modular approach to social recommendation"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "on sampling strategies for neural network based collaborative filtering"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-3 (induced by source paper "attentive knowledge graph embedding for personalized recommendation"):
  with members:
    - "session based recommendation with graph neural network"
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "knowledge aware graph neural networks with label smoothness regularization for recommender systems"
    - "graph neural networks for social recommendation"
    - "knowledge graph convolutional networks for recommender systems"

Connected through hyperedge HE-4 (induced by source paper "modeling embedding dimension correlations via convolutional neural collaborative filtering"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "fast matrix factorization with non uniform weights on missing data"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "outer product based neural collaborative filtering"
    - "adversarial personalized ranking for recommendation"
    - "a simple convolutional generative network for next item recommendation"

Connected through hyperedge HE-5 (induced by source paper "collaborative filtering with a synthetic feedback loop"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "improving textual network learning with variational homophilic embeddings"
    - "neurec on nonlinear transformation for personalized ranking"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "wasserstein autoencoders for collaborative filtering"
    - "outer product based neural collaborative filtering"
    - "top k off policy correction for a reinforce recommender system"
    - "zero shot learning via class conditioned deep generative models"

Connected through hyperedge HE-6 (induced by source paper "jointly learning explainable rules for recommendation with knowledge graph"):
  with members:
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "deep reinforcement learning for page wise recommendations"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "outer product based neural collaborative filtering"
    - "explainable recommendation a survey and new perspectives"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"

Connected through hyperedge HE-7 (induced by source paper "λopt learn to regularize recommender models in finer levels"):
  with members:
    - "warm up cold start advertisements improving ctr predictions via learning to learn id embeddings"
    - "adversarial personalized ranking for recommendation"
    - "aesthetic based clothing recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "entity embeddings of categorical variables"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "an item recommendation approach by fusing images based on neural networks"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"

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)