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: "structure and inference in annotated networks"
  Abstract: Analysis of network structure is usually based on knowledge of connections alone, ignoring additional information such as gender or age of individuals in social networks. Here the authors devise an approach that incorporates such metadata and uses it to improve the detection of network communities.

Connected through hyperedge HE-1 (induced by source paper "on a scalable entropic breaching of the overfitting barrier in machine learning"):
  with members:
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-2 (induced by source paper "community detection in node attributed social networks a survey"):
  with members:
    - "attributed graph clustering a deep attentional embedding approach"
    - "non linear attributed graph clustering by symmetric nmf with pu learning"
    - "efficiently clustering very large attributed graphs"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for meta path based proximity"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "mining application aware community organization with expanded feature subspaces from concerned attributes in social networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "monet debiasing graph embeddings via the metadata orthogonal training unit"):
  with members:
    - "global vectors for node representations"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "compositional fairness constraints for graph embeddings"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "a tutorial on network embeddings"
    - "enhancing network embedding with auxiliary information an explicit matrix factorization perspective"
    - "a survey on network embedding"

Connected through hyperedge HE-4 (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"
    - "block models and personalized pagerank"
    - "the ground truth about metadata and community detection in networks"

Connected through hyperedge HE-5 (induced by source paper "detection of community structures in networks with nodal features based on generative probabilistic approach"):
  with members:
    - "iedc an integrated approach for overlapping and non overlapping community detection"
    - "the ground truth about metadata and community detection in networks"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "general optimization technique for high quality community detection in complex networks"
    - "community detection in networks with node attributes"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "relation strength aware clustering of heterogeneous information networks with incomplete attributes"
    - "social structure of facebook networks"

Connected through hyperedge HE-6 (induced by source paper "mutual information in community detection with covariate information and correlated networks"):
  with members:
    - "the geometry of community detection via the mmse matrix"
    - "community detection with side information exact recovery under the stochastic block model"
    - "information theoretic thresholds from the cavity method"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "phase transitions in community detection a solvable toy model"

Connected through hyperedge HE-7 (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"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "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-8 (induced by source paper "multiscale dynamical embeddings of complex networks"):
  with members:
    - "the many facets of community detection in complex networks"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"
    - "a generalized and adaptive method for community detection"
    - "ranking and clustering of nodes in networks with smart teleportation"
    - "a tutorial on spectral clustering"

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)