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: "synchronization over z2 and community detection in signed multiplex networks with constraints"
  Abstract: Finding group elements from noisy measurements of their pairwise ratios is also known as the group synchronization problem, first introduced in the context of the group SO(2) of planar rotations, whose usefulness has been demonstrated recently in engineering and structural biology. Here, we focus on synchronization over $\mathbb{Z}_2$, and consider the problem of identifying communities in a multiplex network when the interaction between the nodes is described by a signed (possibly weighted) measure of similarity, and when the multiplex network has a natural partition into two communities. When one has the additional information that certain subsets of nodes represent the same unknown group element, we consider and compare several algorithms based on spectral, semidefinite programming (SDP) and message passing algorithms. In other words, all nodes within such a subset represent the same unknown group element, and one has available noisy pairwise measurements between pairs of nodes that belong to different non-overlapping subsets. Following a recent analysis of the eigenvector method for synchronization over SO(2), we analyze the robustness to noise of the eigenvector method for synchronization over $\mathbb{Z}_2$, when the underlying graph of pairwise measurements is the Erd\H{o}s-R\'{e}nyi random graph, using results from the random matrix theory literature. We also propose a message passing synchronization algorithm that outperforms the existing eigenvector synchronization algorithm only for certain classes of graphs and noise models, and enjoys the flexibility of incorporating constraints that may not be easily accommodated by other spectral or SDP-based methods. We apply our methods both to several synthetic models and a real data set of roll call voting patterns in the U.S. Congress across time, to identify the two existing communities, i.e., the Democratic and Republican parties.

Connected through hyperedge HE-1 (induced by source paper "the noise sensitivity phase transition in spectral group synchronization over compact groups"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "spectral synchronization of multiple views in se 3"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "a solution for multi alignment by transformation synchronisation"
    - "cramer rao bounds for synchronization of rotations"
    - "multireference alignment using semidefinite programming"
    - "exact and stable recovery of rotations for robust synchronization"
    - "estimation and registration on graphs"

Connected through hyperedge HE-2 (induced by source paper "multiple partitioning of multiplex signed networks application to european parliament votes"):
  with members:
    - "signed graph analysis for the interpretation of voting behavior"
    - "brazilian congress structural balance analysis"
    - "relevance of negative links in graph partitioning a case study using votes from the european parliament"
    - "generalized measures for the evaluation of community detection methods"
    - "exploiting social network structure for person to person sentiment analysis"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-3 (induced by source paper "non unique games over compact groups and orientation estimation in cryo em"):
  with members:
    - "near optimal joint object matching via convex relaxation"
    - "fast steerable principal component analysis"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "exact recovery in the stochastic block model"
    - "multireference alignment using semidefinite programming"
    - "approximating the little grothendieck problem over the orthogonal and unitary groups"
    - "efficient rounding for the noncommutative grothendieck inequality"

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)