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: "walklets multiscale graph embeddings for interpretable network classification"
  Abstract: We present Walklets, a novel approach for learning multiscale representations of vertices in a network. These representations clearly encode multiscale vertex relationships in a continuous vector space suitable for multi-label classification problems. Unlike previous work, the latent features generated using Walklets are analytically derivable, and human interpretable. #R##N#Walklets uses the offsets between vertices observed in a random walk to learn a series of latent representations, each which captures successively larger relationships. This variety of dependency information allows the same representation strategy to model phenomenon which occur at different scales. #R##N#We demonstrate Walklets' latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that Walklets outperforms new methods based on neural matrix factorization, and can scale to graphs with millions of vertices and edges.

Connected through hyperedge HE-1 (induced by source paper "graph neighborhood attentive pooling"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "simplifying graph convolutional networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "diffusion maps for textual network embedding"
    - "attentive pooling networks"

Connected through hyperedge HE-2 (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"
    - "attributed social network embedding"
    - "semi supervised learning on graphs with generative adversarial nets"

Connected through hyperedge HE-3 (induced by source paper "graph attention auto encoders"):
  with members:
    - "universal transformers"
    - "detecting antagonistic and allied communities on social media"
    - "graph convolutional neural networks for web scale recommender systems"
    - "sentiment driven community profiling and detection on social media"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "harp hierarchical representation learning for networks"

Connected through hyperedge HE-4 (induced by source paper "disentangling interpretable generative parameters of random and real world graphs"):
  with members:
    - "towards interpretable sparse graph representation learning with laplacian pooling"
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning deep generative models of graphs"
    - "geometric deep learning going beyond euclidean data"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-5 (induced by source paper "honem network embedding using higher order patterns in sequential data"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graph embedding techniques applications and performance a survey"
    - "mohone modeling higher order network effects in knowledgegraphs via network infused embeddings"
    - "detecting anomalies in sequential data with higher order networks"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "collaborative similarity embedding for recommender systems"):
  with members:
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "neural collaborative filtering"
    - "vertex context sampling for weighted network embedding"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "item2vec neural item embedding for collaborative filtering"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-7 (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"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-8 (induced by source paper "deep loopy neural network model for graph structured data representation learning"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "on the difficulty of training recurrent 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.SI(Social and Information Networks)