human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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 $G$.

Includes members:
  Member "cramer rao bounds for synchronization of rotations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the distribution of minima in intrinsic metric rotation averaging")
  Member "near optimal joint object matching via convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rgb d individual segmentation")
  Member "exact recovery in the stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff")
  Member "approximating the little grothendieck problem over the orthogonal and unitary groups":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi frequency phase synchronization")
  Member "exact and stable recovery of rotations for robust synchronization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the distribution of minima in intrinsic metric rotation averaging")
  Member "community detection in the labelled stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spectral clustering of signed graphs via matrix power means")
  Member "phase retrieval with polarization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hardness results for structured linear systems")
  Member "correlation clustering with a fixed number of clusters":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spectral clustering of signed graphs via matrix power means")

Question: Please tell me which class the source paper 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)