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"
  Abstract: This paper is concerned with jointly recovering $n$ node-variables $\left\{ x_{i}\right\}_{1\leq i\leq n}$ 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 \emph{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 \emph{minimum} \emph{channel divergence measures} to characterize the degree of measurement corruption, which together with the size of the minimum cut of $\mathcal{G}$ dictates the feasibility of exact information recovery. For various homogeneous graphs, the recovery condition depends almost only on the edge sparsity of the measurement graph irrespective of other graphical metrics; alternatively, the minimum sample complexity required for these graphs scales like \[ \text{minimum sample complexity }\asymp\frac{n\log n}{\mathsf{Hel}_{1/2}^{\min}} \] for certain information metric $\mathsf{Hel}_{1/2}^{\min}$ defined in the main text, as long as the alphabet size is not super-polynomial in $n$. 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 "multi frequency phase synchronization"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "global registration of multiple point clouds using semidefinite programming"
    - "approximating the little grothendieck problem over the orthogonal and unitary groups"

Connected through hyperedge HE-2 (induced by source paper "parity queries for binary classification"):
  with members:
    - "unequal error protection querying policies for the noisy 20 questions problem"
    - "community recovery in hypergraphs"
    - "predicting positive and negative links with noisy queries theory practice"
    - "fundamental limits on data acquisition trade offs between sample complexity and query difficulty"
    - "the set of solutions of random xorsat formulae"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "boolean compressed sensing and noisy group testing"
    - "group testing with probabilistic tests theory design and application"

Connected through hyperedge HE-3 (induced by source paper "community recovery in hypergraphs"):
  with members:
    - "fast algorithms for robust pca via gradient descent"
    - "community recovery in graphs with locality"
    - "uniform hypergraph partitioning provable tensor methods and sampling techniques"
    - "clustering with same cluster queries"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
    - "spectral detection on sparse hypergraphs"

Connected through hyperedge HE-4 (induced by source paper "fundamental limits on data acquisition trade offs between sample complexity and query difficulty"):
  with members:
    - "unequal error protection querying policies for the noisy 20 questions problem"
    - "community recovery in hypergraphs"
    - "collaborative 20 questions for target localization"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-5 (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"

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)