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: "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
  Abstract: We consider the problem of clustering a graph $G$ into two communities by observing a subset of the vertex correlations. Specifically, we consider the inverse problem with observed variables $Y=B_G x \oplus Z$, where $B_G$ is the incidence matrix of a graph $G$, $x$ is the vector of unknown vertex variables (with a uniform prior) and $Z$ is a noise vector with Bernoulli$(\varepsilon)$ i.i.d. entries. All variables and operations are Boolean. This model is motivated by coding, synchronization, and community detection problems. In particular, it corresponds to a stochastic block model or a correlation clustering problem with two communities and censored edges. Without noise, exact recovery (up to global flip) of $x$ is possible if and only the graph $G$ is connected, with a sharp threshold at the edge probability $\log(n)/n$ for Erd\H{o}s-R\'enyi random graphs. The first goal of this paper is to determine how the edge probability $p$ needs to scale to allow exact recovery in the presence of noise. Defining the degree (oversampling) rate of the graph by $\alpha =np/\log(n)$, it is shown that exact recovery is possible if and only if $\alpha >2/(1-2\varepsilon)^2+ o(1/(1-2\varepsilon)^2)$. In other words, $2/(1-2\varepsilon)^2$ is the information theoretic threshold for exact recovery at low-SNR. In addition, an efficient recovery algorithm based on semidefinite programming is proposed and shown to succeed in the threshold regime up to twice the optimal rate. For a deterministic graph $G$, defining the degree rate as $\alpha=d/\log(n)$, where $d$ is the minimum degree of the graph, it is shown that the proposed method achieves the rate $\alpha> 4((1+\lambda)/(1-\lambda)^2)/(1-2\varepsilon)^2+ o(1/(1-2\varepsilon)^2)$, where $1-\lambda$ is the spectral gap of the graph $G$.

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 "exact inference in structured prediction"):
  with members:
    - "learning latent variable structured prediction models with gaussian perturbations"
    - "exact recovery in the stochastic block model"
    - "efficiently searching for frustrated cycles in map inference"
    - "search based structured prediction"

Connected through hyperedge HE-3 (induced by source paper "message passing algorithms for synchronization problems over compact groups"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "phase transitions in sparse pca"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "semidefinite programs on sparse random graphs and their application to community detection"

Connected through hyperedge HE-4 (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"
    - "information recovery from pairwise measurements"
    - "boolean compressed sensing and noisy group testing"
    - "group testing with probabilistic tests theory design and application"

Connected through hyperedge HE-5 (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-6 (induced by source paper "community detection in signed networks an error correcting code approach"):
  with members:
    - "a survey of signed network mining in social media"
    - "clustering and community detection in directed networks a survey"

Connected through hyperedge HE-7 (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"
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "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"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-8 (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"
    - "spectral detection in the censored block model"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "information theoretically optimal sparse pca"
    - "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"

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)