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 network layers with the strata multilayer stochastic block model"
  Abstract: Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gives rise to a layer. While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes. To concisely extract information from a multilayer network, we propose to identify and combine sets of layers with meaningful similarities in community structure. In this paper, we describe the “strata multilayer stochastic block model” (sMLSBM), a probabilistic model for multilayer community structure. The central extension of the model is that there exist groups of layers, called “strata”, which are defined such that all layers in a given stratum have community structure described by a common stochastic block model (SBM). That is, layers in a stratum exhibit similar node-to-community assignments and SBM probability parameters. Fitting the sMLSBM to a multilayer network provides a joint clustering that yields node-to-community and layer-to-stratum assignments, which cooperatively aid one another during inference. We describe an algorithm for separating layers into their appropriate strata and an inference technique for estimating the SBM parameters for each stratum. We demonstrate our method using synthetic networks and a multilayer network inferred from data collected in the Human Microbiome Project.

Connected through hyperedge HE-1 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk"):
  with members:
    - "multi relational poincar e graph embeddings"
    - "representation learning for attributed multiplex heterogeneous network"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "principled multilayer network embedding"
    - "a survey on network embedding"
    - "poincar e embeddings for learning hierarchical representations"
    - "representation learning for scale free networks"

Connected through hyperedge HE-2 (induced by source paper "role detection in bicycle sharing networks using multilayer stochastic block models"):
  with members:
    - "relating modularity maximization and stochastic block models in multilayer networks"
    - "community discovery in dynamic networks a survey"
    - "a multi scale analysis of 27 000 urban street networks every us city town urbanized area and zillow neighborhood"
    - "random graph models for dynamic networks"
    - "the ground truth about metadata and community detection in networks"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "generative benchmark models for mesoscale structures in multilayer networks"

Connected through hyperedge HE-3 (induced by source paper "community detection and improved detectability in multiplex networks"):
  with members:
    - "detectability thresholds of general modular graphs"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "finding overlapping communities in multiplex networks"
    - "structure and inference in annotated networks"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "community detection in networks with unequal groups"
    - "information diffusion of topic propagation in social media"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-4 (induced by source paper "modularity in multilayer networks using redundancy based resolution and projection based inter layer coupling"):
  with members:
    - "consensus community detection in multilayer networks using parameter free graph pruning"
    - "revisiting resolution and inter layer coupling factors in modularity for multilayer networks"
    - "modularity in complex multilayer networks with multiple aspects a static perspective"
    - "community detection in multiplex networks using locally adaptive random walks"
    - "a classification for community discovery methods in complex networks"

Connected through hyperedge HE-5 (induced by source paper "fast embedding of multilayer networks an algorithm and application to group fmri"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graph embedding techniques applications and performance a survey"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "principled multilayer network embedding"):
  with members:
    - "spectral clustering methods for multiplex networks"
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "name disambiguation in anonymized graphs using network embedding"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "multilayer spectral graph clustering via convex layer aggregation"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"
    - "trust from the past bayesian personalized ranking based link prediction in knowledge graphs"

Connected through hyperedge HE-7 (induced by source paper "random graph models for dynamic networks"):
  with members:
    - "stability of spreading processes over time varying large scale networks"
    - "stochastic block transition models for dynamic networks"
    - "nonparametric multi group membership model for dynamic networks"
    - "dynamic stochastic blockmodels statistical models for time evolving networks"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

Connected through hyperedge HE-8 (induced by source paper "post processing partitions to identify domains of modularity optimization"):
  with members:
    - "the many facets of community detection in 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)