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: "block models and personalized pagerank"
  Abstract: Methods for ranking the importance of nodes in a network have a rich history in machine learning and across domains that analyze structured data. Recent work has evaluated these methods through the “seed set expansion problem”: given a subset   S   of nodes from a community of interest in an underlying graph, can we reliably identify the rest of the community? We start from the observation that the most widely used techniques for this problem, personalized PageRank and heat kernel methods, operate in the space of “landing probabilities” of a random walk rooted at the seed set, ranking nodes according to weighted sums of landing probabilities of different length walks. Both schemes, however, lack an a priori relationship to the seed set objective. In this work, we develop a principled framework for evaluating ranking methods by studying seed set expansion applied to the stochastic block model. We derive the optimal gradient for separating the landing probabilities of two classes in a stochastic block model and find, surprisingly, that under reasonable assumptions the gradient is asymptotically equivalent to personalized PageRank for a specific choice of the PageRank parameter   α   that depends on the block model parameters. This connection provides a formal motivation for the success of personalized PageRank in seed set expansion and node ranking generally. We use this connection to propose more advanced techniques incorporating higher moments of landing probabilities; our advanced methods exhibit greatly improved performance, despite being simple linear classification rules, and are even competitive with belief propagation.

Connected through hyperedge HE-1 (induced by source paper "diffusion improves graph learning"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "simplifying graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "optimizing generalized pagerank methods for seed expansion community detection"
    - "node embedding with adaptive similarities for scalable learning over graphs"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-2 (induced by source paper "stochastic block models are a discrete surface tension"):
  with members:
    - "community detection in networks via nonlinear modularity eigenvectors"
    - "unifying sparsest cut cluster deletion and modularity clustering objectives with correlation clustering"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm"
    - "uncertainty quantification in graph based classification of high dimensional data"
    - "efficient method for estimating the number of communities in a network"
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"

Connected through hyperedge HE-3 (induced by source paper "optimizing generalized pagerank methods for seed expansion community detection"):
  with members:
    - "landing probabilities of random walks for seed set expansion in hypergraphs"
    - "quadratic decomposable submodular function minimization theory and practice"
    - "diffusion improves graph learning"
    - "mean field analysis of personalized pagerank with implications for local graph clustering"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "finding cheeger cuts in hypergraphs via heat equation"
    - "snap a general purpose network analysis and graph mining library"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for task driven discovery of incomplete networks"):
  with members:
    - "partitioning graphs for the cloud using reinforcement learning"
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "deep learning on graphs a survey"
    - "gemsec graph embedding with self clustering"
    - "selective harvesting over networks"
    - "learning combinatorial optimization algorithms over graphs"
    - "emergence of locomotion behaviours in rich environments"

Connected through hyperedge HE-5 (induced by source paper "landing probabilities of random walks for seed set expansion in hypergraphs"):
  with members:
    - "random walks on hypergraphs with edge dependent vertex weights"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "optimizing generalized pagerank methods for seed expansion community detection"
    - "quadratic decomposable submodular function minimization"
    - "inhomogoenous hypergraph clustering with applications"
    - "the spacey random walk a stochastic process for higher order data"
    - "higher order organization of complex networks"
    - "motif clustering and overlapping clustering for social network analysis"

Connected through hyperedge HE-6 (induced by source paper "targeted sampling from massive blockmodel graphs with personalized pagerank"):
  with members:
    - "pagerank beyond the web"
    - "oriented and degree generated block models generating and inferring communities with inhomogeneous degree distributions"
    - "finding sparse cuts locally using evolving sets"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-7 (induced by source paper "node embedding with adaptive similarities for scalable learning over graphs"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "graph embedding techniques applications and performance a survey"
    - "struc2vec learning node representations from structural identity"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "convolutional 2d knowledge graph embeddings"
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-8 (induced by source paper "random walks on simplicial complexes and the normalized hodge 1 laplacian"):
  with members:
    - "graph based semi supervised active learning for edge flows"
    - "flow smoothing and denoising graph signal processing in the edge space"
    - "simplicial closure and higher order link prediction"
    - "centralities in simplicial complexes"
    - "centrality measures for graphons accounting for uncertainty in networks"
    - "the many facets of community detection in complex networks"
    - "towards spectral sparsification of simplicial complexes based on generalized effective resistance"
    - "higher order organization of complex 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)