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: "topological recurrent neural network for diffusion prediction"
  Abstract: In this paper, we study the problem of using representation learning to assist information diffusion prediction on graphs. In particular, we aim at estimating the probability of an inactive node to be activated next in a cascade. Despite the success of recent deep learning methods for diffusion, we find that they often underexplore the cascade structure. We consider a cascade as not merely a sequence of nodes ordered by their activation time stamps; instead, it has a richer structure indicating the diffusion process over the data graph. As a result, we introduce a new data model, namely diffusion topologies, to fully describe the cascade structure. We find it challenging to model diffusion topologies, which are dynamic directed acyclic graphs (DAGs), with the existing neural networks. Therefore, we propose a novel topological recurrent neural network, namely Topo-LSTM, for modeling dynamic DAGs. We customize Topo-LSTM for the diffusion prediction task, and show it improves the state-of-the-art baselines, by 20.1%--56.6% (MAP) relatively, across multiple real-world data sets. Our code and data sets are available online at this https URL

Connected through hyperedge HE-1 (induced by source paper "deep collaborative embedding for information cascade prediction"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "iad interaction aware diffusion framework in social networks"
    - "deepinf social influence prediction with deep learning"
    - "attributed social network embedding"
    - "collaborative inference of coexisting information diffusions"
    - "a survey on network embedding"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "bl mne emerging heterogeneous social network embedding through broad learning with aligned autoencoder"

Connected through hyperedge HE-2 (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"
    - "dynamic graph representation learning via self attention networks"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "motif based convolutional neural network on graphs"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "popularity prediction on social platforms with coupled graph neural networks"):
  with members:
    - "graph wavelet neural network"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deepinf social influence prediction with deep learning"
    - "how powerful are graph neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-4 (induced by source paper "relation learning on social networks with multi modal graph edge variational autoencoders"):
  with members:
    - "i know you ll be back interpretable new user clustering and churn prediction on a mobile social application"
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "neural embedding propagation on heterogeneous networks"
    - "cubenet multi facet hierarchical heterogeneous network construction analysis and mining"
    - "representation learning on graphs with jumping knowledge networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "relationship profiling over social networks reverse smoothness from similarity to closeness"
    - "metapad meta pattern discovery from massive text corpora"

Connected through hyperedge HE-5 (induced by source paper "influence maximization via representation learning"):
  with members:
    - "balancing information exposure in social networks"
    - "node2vec scalable feature learning for networks"
    - "deepcas an end to end predictor of information cascades"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-6 (induced by source paper "neural diffusion model for microscopic cascade prediction"):
  with members:
    - "attention is all you need"
    - "who will share my image predicting the content diffusion path in online social networks"
    - "semi supervised classification with graph convolutional networks"
    - "deepcas an end to end predictor of information cascades"
    - "deep residual learning for image recognition"
    - "the simple rules of social contagion"
    - "line large scale information network embedding"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-7 (induced by source paper "dynamic network model from partial observations"):
  with members:
    - "deepcas an end to end predictor of information cascades"
    - "structure and dynamics of information pathways in online media"
    - "submodular inference of diffusion networks from multiple trees"
    - "uncovering the temporal dynamics of diffusion networks"
    - "on the convexity of latent social network inference"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"

Connected through hyperedge HE-8 (induced by source paper "a comprehensive survey of graph embedding problems techniques and applications"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "struc2vec learning node representations from structural identity"
    - "geometric deep learning going beyond euclidean data"
    - "graph based isometry invariant representation learning"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "on the representation and embedding of knowledge bases beyond binary relations"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "gated graph sequence neural networks"

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)