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: "attributed social network embedding"
  Abstract: Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship networks and textual content of citation networks. These rich attribute information of social actors reveal the homophily effect, exerting huge impacts on the formation of social networks. In this paper, we explore the rich evidence source of attributes in social networks to improve network embedding. We propose a generic Attributed Social Network Embedding framework ( ASNE ), which learns representations for social actors (i.e., nodes) by preserving both the  structural proximity  and  attribute proximity . While the  structural proximity  captures the global network structure, the  attribute proximity  accounts for the homophily effect. To justify our proposal, we conduct extensive experiments on four real-world social networks. Compared to the state-of-the-art network embedding approaches,  ASNE  can learn more informative representations, achieving substantial gains on the tasks of link prediction and node classification. Specifically,  ASNE  significantly outperforms   node2vec  with an 8.2 percent relative improvement on the link prediction task, and a 12.7 percent gain on the node classification task.

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"
    - "collaborative inference of coexisting information diffusions"
    - "a survey on network embedding"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "topological recurrent neural network for diffusion prediction"
    - "bl mne emerging heterogeneous social network embedding through broad learning with aligned autoencoder"

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"
    - "dynamic graph representation learning via self attention networks"
    - "network representation learning a survey"
    - "a survey on network embedding"

Connected through hyperedge HE-3 (induced by source paper "a block based generative model for attributed networks embedding"):
  with members:
    - "adversarially regularized graph autoencoder for graph embedding"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "stochastic block model and exploratory analysis in signed networks"
    - "deepwalk online learning of social representations"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-4 (induced by source paper "anomalydae dual autoencoder for anomaly detection on attributed networks"):
  with members:
    - "spamhmm sparse mixture of hidden markov models for graph connected entities"
    - "heterogeneous information network embedding for recommendation"
    - "semi supervised classification with graph convolutional networks"
    - "scalable anomaly ranking of attributed neighborhoods"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "deep unified multimodal embeddings for understanding both content and users in social media networks"):
  with members:
    - "integrating text and image determining multimodal document intent in instagram posts"
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "universal dependency parsing from scratch"
    - "deep learning based recommender system a survey and new perspectives"
    - "exploring the limits of weakly supervised pretraining"
    - "a comprehensive survey of deep learning for image captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "characterizing and detecting hateful users on twitter"

Connected through hyperedge HE-6 (induced by source paper "deep representation learning for social network analysis"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "hierarchical graph representation learning with differentiable pooling"
    - "techniques for interpretable machine learning"
    - "methods for interpreting and understanding deep neural networks"
    - "spine structural identity preserved inductive network embedding"
    - "representation learning on graphs methods and applications"
    - "heterogeneous information network embedding for recommendation"

Connected through hyperedge HE-7 (induced by source paper "learning graph embedding with adversarial training methods"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "a comprehensive survey on graph neural networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "semi supervised learning on graphs with generative adversarial nets"
    - "network representation learning a survey"

Connected through hyperedge HE-8 (induced by source paper "aligraph a comprehensive graph neural network platform"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graph networks as learnable physics engines for inference and control"
    - "graph embedding techniques applications and performance a survey"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "one shot relational learning for knowledge graphs"
    - "struc2vec learning node representations from structural identity"
    - "an attention based collaboration framework for multi view network representation learning"
    - "principled multilayer network embedding"

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.SI(Social and Information Networks)