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: "clustering and community detection in directed networks a survey"
  Abstract: Abstract   Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed — in the sense that there is directionality on the edges, making the semantics of the edges nonsymmetric as the source node transmits some property to the target one but not vice versa. An interesting feature that real networks present is the clustering or community structure property, under which the graph topology is organized into modules commonly called communities or clusters. The essence here is that nodes of the same community are highly similar while on the contrary, nodes across communities present low similarity. Revealing the underlying community structure of directed complex networks has become a crucial and interdisciplinary topic with a plethora of relevant application domains. Therefore, naturally there is a recent wealth of research production in the area of mining directed graphs — with clustering being the primary method sought and the primary tool for community detection and evaluation. The goal of this paper is to offer an in-depth comparative review of the methods presented so far for clustering directed networks along with the relevant necessary methodological background and also related applications. The survey commences by offering a concise review of the fundamental concepts and methodological base on which graph clustering algorithms capitalize on. Then we present the relevant work along two orthogonal classifications. The first one is mostly concerned with the methodological principles of the clustering algorithms, while the second one approaches the methods from the viewpoint regarding the properties of a good cluster in a directed network. Further, we present methods and metrics for evaluating graph clustering results, demonstrate interesting application domains and provide promising future research directions.

Connected through hyperedge HE-1 (induced by source paper "a survey of community search over big graphs"):
  with members:
    - "exploring communities in large profiled graphs"
    - "efficient algorithms for densest subgraph discovery"
    - "community discovery in dynamic networks a survey"
    - "pp dblp modeling and generating attributed public private networks with dblp"
    - "an optimal and progressive approach to online search of top k influential communities"
    - "local community detection in dynamic networks"
    - "diversified coherent core search on multi layer graphs"
    - "network community detection a review and visual survey"

Connected through hyperedge HE-2 (induced by source paper "gravity inspired graph autoencoders for directed link prediction"):
  with members:
    - "a degeneracy framework for scalable graph autoencoders"
    - "d vae a variational autoencoder for directed acyclic graphs"
    - "simplifying graph convolutional networks"
    - "matrix completion with variational graph autoencoders application in hyperlocal air quality inference"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "learning to make predictions on graphs with autoencoders"

Connected through hyperedge HE-3 (induced by source paper "deep node ranking structural network embedding and end to end node classification"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graph embedding techniques applications and performance a survey"
    - "attention is all you need"
    - "predicting multicellular function through multi layer tissue networks"
    - "a unified approach to interpreting model predictions"
    - "a survey on network embedding"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "higher order organization of complex networks"

Connected through hyperedge HE-4 (induced by source paper "discovering communities of community discovery"):
  with members:
    - "a survey of community search over big graphs"
    - "community structure a comparative evaluation of community detection methods"
    - "community discovery in dynamic networks a survey"
    - "labelrank a stabilized label propagation algorithm for community detection in networks"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "demon a local first discovery method for overlapping communities"
    - "a classification for community discovery methods in complex networks"
    - "community detection via semi synchronous label propagation algorithms"

Connected through hyperedge HE-5 (induced by source paper "friendship paradox biases perceptions in directed networks"):
  with members:
    - "what do your friends think efficient polling methods for networks using friendship paradox"
    - "do diffusion protocols govern cascade growth"
    - "centrality friendship paradoxes when our friends are more important than us"
    - "the majority illusion in social networks"
    - "the h index paradox your coauthors have a higher h index than you do"
    - "network weirdness exploring the origins of network paradoxes"
    - "generalized friendship paradox in complex networks the case of scientific collaboration"
    - "friendship paradox redux your friends are more interesting than you"

Connected through hyperedge HE-6 (induced by source paper "bayesian estimation of the latent dimension and communities in stochastic blockmodels"):
  with members:
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "estimating the number of communities in a network"
    - "exact recovery in the stochastic block model"
    - "pseudo likelihood methods for community detection in large sparse networks"
    - "dynamic stochastic blockmodels statistical models for time evolving networks"
    - "a tutorial on spectral clustering"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-7 (induced by source paper "debiasing community detection the importance of lowly connected nodes"):
  with members:
    - "characterizing the 2016 russian ira influence campaign"
    - "latent space model for multi modal social data"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "community detection in networks with node attributes"
    - "demon a local first discovery method for overlapping communities"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-8 (induced by source paper "multiscale evolutionary perturbation attack on community detection"):
  with members:
    - "adversarial attacks on node embeddings via graph poisoning"
    - "ga based q attack on community detection"
    - "hiding individuals and communities in a social network"
    - "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "multistep greedy algorithm identifies community structure in real world and computer generated 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)