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 coevolutionary network embedding user and item features for recommendation"
  Abstract: Recommender systems often use latent features to explain the behaviors of users and capture the properties of items. As users interact with different items over time, user and item features can influence each other, evolve and co-evolve over time. The compatibility of user and item's feature further influence the future interaction between users and items. Recently, point process based models have been proposed in the literature aiming to capture the temporally evolving nature of these latent features. However, these models often make strong parametric assumptions about the evolution process of the user and item latent features, which may not reflect the reality, and has limited power in expressing the complex and nonlinear dynamics underlying these processes. To address these limitations, we propose a novel deep coevolutionary network model (DeepCoevolve), for learning user and item features based on their interaction graph. DeepCoevolve use recurrent neural network (RNN) over evolving networks to define the intensity function in point processes, which allows the model to capture complex mutual influence between users and items, and the feature evolution over time. We also develop an efficient procedure for training the model parameters, and show that the learned models lead to significant improvements in recommendation and activity prediction compared to previous state-of-the-arts parametric models.

Connected through hyperedge HE-1 (induced by source paper "predicting dynamic embedding trajectory in temporal interaction networks"):
  with members:
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "leveraging friendship networks for dynamic link prediction in social interaction networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "community interaction and conflict on the web"
    - "graph embedding techniques applications and performance a survey"

Connected through hyperedge HE-2 (induced by source paper "hierarchical temporal convolutional networks for dynamic recommender systems"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "neural collaborative filtering"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "recurrent dropout without memory loss"

Connected through hyperedge HE-3 (induced by source paper "an end to end neighborhood based interaction model for knowledge enhanced recommendation"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "kb4rec a dataset for linking knowledge bases with recommender systems"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "nais neural attentive item similarity model for recommendation"
    - "graphgan graph representation learning with generative adversarial nets"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-4 (induced by source paper "learning dynamic embeddings from temporal interactions"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "leveraging friendship networks for dynamic link prediction in social interaction networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "graph embedding techniques applications and performance a survey"
    - "representation learning over dynamic graphs"
    - "predicting interactions between individuals with structural and dynamical information"

Connected through hyperedge HE-5 (induced by source paper "graph embedding techniques applications and performance a survey"):
  with members:
    - "harp hierarchical representation learning for networks"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "walklets multiscale graph embeddings for interpretable network classification"

Connected through hyperedge HE-6 (induced by source paper "capturing edge attributes via network embedding"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "dyngem deep embedding method for dynamic graphs"
    - "graph embedding techniques applications and performance a survey"
    - "struc2vec learning node representations from structural identity"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "graph2vec learning distributed representations of graphs"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-7 (induced by source paper "dyngem deep embedding method for dynamic graphs"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "node2vec scalable feature learning for networks"
    - "learning convolutional neural networks for graphs"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "net2net accelerating learning via knowledge transfer"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-8 (induced by source paper "know evolve deep temporal reasoning for dynamic knowledge graphs"):
  with members:
    - "probabilistic reasoning via deep learning neural association models"
    - "steering opinion dynamics in information diffusion networks"
    - "discriminative embeddings of latent variable models for structured data"
    - "session based recommendations with recurrent neural networks"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "predicting the co evolution of event and knowledge graphs"
    - "shaping social activity by incentivizing users"
    - "embedding entities and relations for learning and inference in knowledge bases"

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)