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: "spectral detection in the censored block model"
  Abstract: We consider the problem of partially recovering hidden binary variables from the observation of (few) censored edge weights, a problem with applications in community detection, correlation clustering and synchronization. We describe two spectral algorithms for this task based on the non-backtracking and the Bethe Hessian operators. These algorithms are shown to be asymptotically optimal for the partial recovery problem, in that they detect the hidden assignment as soon as it is information theoretically possible to do so.

Connected through hyperedge HE-1 (induced by source paper "spectral clustering of signed graphs via matrix power means"):
  with members:
    - "balance in signed networks"
    - "an mbo scheme for clustering and semi supervised clustering of signed networks"
    - "node classification for signed networks using diffuse interface methods"
    - "signed graph convolutional network"
    - "on spectral partitioning of signed graphs"
    - "a modularity based spectral method for simultaneous community and anti community detection"
    - "sne signed network embedding"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"

Connected through hyperedge HE-2 (induced by source paper "adaptive path interpolation for sparse systems application to a simple censored block model"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "entropy and mutual information in models of deep neural networks"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "information theoretic thresholds from the cavity method"
    - "the layered structure of tensor estimation and its mutual information"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"

Connected through hyperedge HE-3 (induced by source paper "incremental eigenpair computation for graph laplacian matrices theory and applications"):
  with members:
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "principled multilayer network embedding"
    - "name disambiguation in anonymized graphs using network embedding"
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "nous construction and querying of dynamic knowledge graphs"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "trust from the past bayesian personalized ranking based link prediction in knowledge graphs"

Connected through hyperedge HE-4 (induced by source paper "phase transitions and a model order selection criterion for spectral graph clustering"):
  with members:
    - "estimating the number of communities in a network"
    - "universal phase transition in community detectability under a stochastic block model"
    - "local set based graph signal reconstruction"
    - "incremental method for spectral clustering of increasing orders"
    - "signal recovery on graphs variation minimization"
    - "multi layer graph analysis for dynamic social networks"
    - "exact recovery in the stochastic block model"
    - "phase transitions in spectral community detection"

Connected through hyperedge HE-5 (induced by source paper "amos an automated model order selection algorithm for spectral graph clustering"):
  with members:
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "local set based graph signal reconstruction"
    - "signal recovery on graphs variation minimization"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "multi layer graph analysis for dynamic social networks"
    - "phase transitions in spectral community detection"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-6 (induced by source paper "mutual information in rank one matrix estimation"):
  with members:
    - "phase transitions in sparse pca"
    - "asymptotic mutual information for the two groups stochastic block model"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "information theoretically optimal sparse pca"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "community detection thresholds and the weak ramanujan property"
    - "iterative estimation of constrained rank one matrices in noise"
    - "tight bounds on the capacity of binary input random cdma systems"

Connected through hyperedge HE-7 (induced by source paper "clustering from sparse pairwise measurements"):
  with members:
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "community detection in the labelled stochastic block model"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "optimality of belief propagation for crowdsourced classification"):
  with members:
    - "achieving budget optimality with adaptive schemes in crowdsourcing"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "max product belief propagation for linear programming applications to combinatorial optimization"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "matrix completion from a few entries"

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)