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: "latent multi group membership graph model"
  Abstract: We develop the Latent Multi-group Membership Graph (LMMG) model, a model of networks with rich node feature structure. In the LMMG model, each node belongs to multiple groups and each latent group models the occurrence of links as well as the node feature structure. The LMMG can be used to summarize the network structure, to predict links between the nodes, and to predict missing features of a node. We derive efficient inference and learning algorithms and evaluate the predictive performance of the LMMG on several social and document network datasets.

Connected through hyperedge HE-1 (induced by source paper "struct mmsb mixed membership stochastic blockmodels with interpretable structured priors"):
  with members:
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "snap a general purpose network analysis and graph mining library"
    - "discriminative nonparametric latent feature relational models with data augmentation"
    - "spectral networks and locally connected networks on graphs"
    - "community detection in networks with node attributes"
    - "the nonparametric metadata dependent relational model"

Connected through hyperedge HE-2 (induced by source paper "predicting attributes of nodes using network structure"):
  with members:
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"
    - "learning graph representations with embedding propagation"
    - "node2vec scalable feature learning for networks"
    - "recurrent neural networks for multivariate time series with missing values"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "community detection in networks with node attributes"
    - "social structure of facebook networks"

Connected through hyperedge HE-3 (induced by source paper "contextual stochastic block models"):
  with members:
    - "algorithmic decision making and the cost of fairness"
    - "clustering attributed graphs models measures and methods"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "statistical estimation from denoising to sparse regression and hidden cliques"
    - "partitioning networks with node attributes by compressing information flow"
    - "community detection in networks with node attributes"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-4 (induced by source paper "snap a general purpose network analysis and graph mining library"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "ringo interactive graph analytics on big memory machines"
    - "network lasso clustering and optimization in large graphs"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "nonparametric multi group membership model for dynamic networks"
    - "community detection in networks with node attributes"
    - "discovering social circles in ego networks"
    - "structure and dynamics of information pathways in online media"

Connected through hyperedge HE-5 (induced by source paper "network cartography seeing the forest and the trees"):
  with members:
    - "role discovery in networks"
    - "community detection in networks with node attributes"
    - "mining attribute structure correlated patterns in large attributed graphs"

Connected through hyperedge HE-6 (induced by source paper "community detection in networks with node attributes"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "relation strength aware clustering of heterogeneous information networks with incomplete attributes"
    - "demon a local first discovery method for overlapping communities"
    - "structure and overlaps of communities in networks"
    - "efficient community detection in large networks using content and links"
    - "neighborhoods are good communities"
    - "infinite multiple membership relational modeling for complex networks"

Connected through hyperedge HE-7 (induced by source paper "nonparametric multi group membership model for dynamic networks"):
  with members:
    - "an infinite latent attribute model for network data"
    - "infinite multiple membership relational modeling for complex networks"
    - "modeling social networks with node attributes using the multiplicative attribute graph model"
    - "multiplicative attribute graph model of real world networks"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-8 (induced by source paper "scalable text and link analysis with mixed topic link models"):
  with members:
    - "latent topic models for hypertext"
    - "a bayesian framework for community detection integrating content and link"
    - "active learning for node classification in assortative and disassortative networks"
    - "efficient and principled method for detecting communities in 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)