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: "multiplicative attribute graph model of real world networks"
  Abstract: Large scale real-world network data such as social and information networks are ubiquitous. The study of such social and information networks seeks to find patterns and explain their emergence through tractable models. In most networks, and especially in social networks, nodes have a rich set of attributes (e.g., age, gender) associated with them. #R##N#Here we present a model that we refer to as the Multiplicative Attribute Graphs (MAG), which naturally captures the interactions between the network structure and the node attributes. We consider a model where each node has a vector of categorical latent attributes associated with it. The probability of an edge between a pair of nodes then depends on the product of individual attribute-attribute affinities. The model yields itself to mathematical analysis and we derive thresholds for the connectivity and the emergence of the giant connected component, and show that the model gives rise to networks with a constant diameter. We analyze the degree distribution to show that MAG model can produce networks with either log-normal or power-law degree distributions depending on certain conditions.

Connected through hyperedge HE-1 (induced by source paper "graph generators state of the art and open challenges"):
  with members:
    - "a survey on theoretical advances of community detection in networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "gmark schema driven generation of graphs and queries"
    - "motifs in temporal networks"
    - "g core a core for future graph query languages"
    - "darwini generating realistic large scale social graphs"

Connected through hyperedge HE-2 (induced by source paper "a statistical model for dynamic networks with neural variational inference"):
  with members:
    - "a generative model for dynamic networks with applications"
    - "adam a method for stochastic optimization"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "stochastic block transition models for dynamic networks"
    - "nonparametric multi group membership model for dynamic networks"

Connected through hyperedge HE-3 (induced by source paper "l2p an algorithm for estimating heavy tailed outcomes"):
  with members:
    - "ell_1 regression with heavy tailed distributions"
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"
    - "virality prediction and community structure in social networks"
    - "smote synthetic minority over sampling technique"
    - "learning to order things"

Connected through hyperedge HE-4 (induced by source paper "node isolation in large homogeneous binary multiplicative attribute graph models"):
  with members:
    - "modeling social networks with node attributes using the multiplicative attribute graph model"

Connected through hyperedge HE-5 (induced by source paper "network model selection for task focused attributed network inference"):
  with members:
    - "evaluating social networks using task focused network inference"
    - "network structure inference a survey motivations methods and applications"
    - "the role of social networks in information diffusion"
    - "learning attitudes and attributes from multi aspect reviews"
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-6 (induced by source paper "evaluating social networks using task focused network inference"):
  with members:
    - "network structure inference a survey motivations methods and applications"
    - "inferring networks of substitutable and complementary products"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-7 (induced by source paper "when labels fall short property graph simulation via blending of network structure and vertex attributes"):
  with members:
    - "multi centrality graph spectral decompositions and their application to cyber intrusion detection"

Connected through hyperedge HE-8 (induced by source paper "asymptotic degree distributions in large homogeneous random networks a little theory and a counterexample"):
  with members:
    - "on the log normality of the degree distribution in large homogeneous binary multiplicative attribute graph models"

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)