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: "information recovery from pairwise measurements a shannon theoretic approach"
  Abstract: This paper is concerned with jointly recovering $n$ node-variables $\{ x_i \} _{1\leq i\leq n}$ from a collection of pairwise difference measurements. Imagine we acquire a few observations taking the form of $x_i-x_j$; the observation pattern is represented by a measurement graph $\mathcal{G}$ with an edge set $\mathcal{E}$ such that $x_i-x_j$ is observed if and only if $(i,j)\in\mathcal{E}$. To account for noisy measurements in a general manner, we model the data acquisition process by a set of channels with given input/output transition measures. Employing information-theoretic tools applied to channel decoding problems, we develop a unified framework to characterize the fundamental recovery criterion, which accommodates general graph structures, alphabet sizes, and channel transition measures. In particular, our results isolate a family of minimum channel divergence measures to characterize the degree of measurement corruption, which together with the minimum cut size of $\mathcal{G}$ dictates the feasibility of exact information recovery. For various homogeneous graphs, the recovery condition depends almost only on the edge sparsity irrespective of other graphical metrics. We apply our general theory to three concrete applications, including the stochastic block model, the outlier model, and the haplotype assembly problem. Our theory leads to order-wise tight recovery conditions for all these scenarios.

Connected through hyperedge HE-1 (induced by source paper "the projected power method an efficient algorithm for joint alignment from pairwise differences"):
  with members:
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "fast algorithms for robust pca via gradient descent"
    - "f divergence inequalities"
    - "community recovery in graphs with locality"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"

Connected through hyperedge HE-2 (induced by source paper "community recovery in graphs with locality"):
  with members:
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
    - "optimal haplotype assembly from high throughput mate pair reads"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "near optimal joint object matching via convex relaxation"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-3 (induced by source paper "clustering from sparse pairwise measurements"):
  with members:
    - "spectral detection in the censored block model"
    - "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-4 (induced by source paper "information recovery from pairwise measurements"):
  with members:
    - "community recovery in graphs with locality"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "bounds among f divergences"
    - "discrete signal processing on graphs sampling theory"
    - "optimal haplotype assembly from high throughput mate pair reads"
    - "near optimal joint object matching via convex relaxation"
    - "accurate community detection in the stochastic block model via spectral algorithms"

Connected through hyperedge HE-5 (induced by source paper "spectral mle top k rank aggregation from pairwise comparisons"):
  with members:
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "learning mixtures of ranking models"
    - "renyi divergence and kullback leibler divergence"
    - "a statistical decision theoretic framework for social choice"
    - "multiple identifications in multi armed bandits"
    - "active ranking using pairwise comparisons"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-6 (induced by source paper "sketching for sequential change point detection"):
  with members:
    - "how to fake multiply by a gaussian matrix"
    - "change detection with compressive measurements"
    - "sketching as a tool for numerical linear algebra"
    - "local convergence of an algorithm for subspace identification from partial data"
    - "quickest search over multiple sequences with mixed observations"
    - "compressed hypothesis testing to mix or not to mix"

Connected through hyperedge HE-7 (induced by source paper "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"):
  with members:
    - "asymptotic mutual information for the two groups stochastic block model"
    - "renyi divergence and kullback leibler divergence"
    - "exact recovery in the stochastic block model"
    - "pseudo likelihood methods for community detection in large sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "community detection in the labelled stochastic block model"

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.IT(Information Theory)