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: "dynamic graph representation learning via self attention networks"
  Abstract: Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualization. Previous methods on graph representation learning mainly focus on static graphs, however, many real-world graphs are dynamic and evolve over time. In this paper, we present Dynamic Self-Attention Network (DySAT), a novel neural architecture that operates on dynamic graphs and learns node representations that capture both structural properties and temporal evolutionary patterns. Specifically, DySAT computes node representations by jointly employing self-attention layers along two dimensions: structural neighborhood and temporal dynamics. We conduct link prediction experiments on two classes of graphs: communication networks and bipartite rating networks. Our experimental results show that DySAT has a significant performance gain over several different state-of-the-art graph embedding baselines.

Connected through hyperedge HE-1 (induced by source paper "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction"):
  with members:
    - "an induced multi relational framework for answer selection in community question answer platforms"
    - "deepinf social influence prediction with deep learning"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "motif based convolutional neural network on graphs"
    - "topological recurrent neural network for diffusion prediction"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "exem expert embedding using dominating set theory with deep learning approaches"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "community aware random walk for network embedding"
    - "dynnode2vec scalable dynamic network embedding"
    - "graph embedding techniques applications and performance a survey"
    - "attributed social network embedding"
    - "network representation learning a survey"
    - "a survey on network embedding"

Connected through hyperedge HE-3 (induced by source paper "discovering strategic behaviors for collaborative content production in social networks"):
  with members:
    - "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction"
    - "an induced multi relational framework for answer selection in community question answer platforms"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "session based social recommendation via dynamic graph attention networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "attention models in graphs a survey"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-4 (induced by source paper "learning graph convolutional network for skeleton based human action recognition by neural searching"):
  with members:
    - "video action recognition via neural architecture searching"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "darts differentiable architecture search"

Connected through hyperedge HE-5 (induced by source paper "variational graph recurrent neural networks"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "semi implicit graph variational auto encoders"
    - "dyngem deep embedding method for dynamic graphs"
    - "learning structural node embeddings via diffusion wavelets"
    - "struc2vec learning node representations from structural identity"
    - "timers error bounded svd restart on dynamic networks"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-6 (induced by source paper "an induced multi relational framework for answer selection in community question answer platforms"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "revisiting semi supervised learning with graph embeddings"
    - "network structure inference a survey motivations methods and applications"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"

Connected through hyperedge HE-7 (induced by source paper "motif based convolutional neural network on graphs"):
  with members:
    - "attention is all you need"
    - "improving latent user models in online social media"
    - "detecting strong ties using network motifs"
    - "high order stochastic graphlet embedding for graph based pattern recognition"
    - "robust spatial filtering with graph convolutional neural networks"
    - "higher order organization of complex networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

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)