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: "eigenvector synchronization graph rigidity and the molecule problem"
  Abstract: The graph realization problem has received a great deal of attention in recent years, due to its importance in applications such as wireless sensor networks and structural biology. In this paper, we extend the previous work and propose the 3D-As-Synchronized-As-Possible (3D-ASAP) algorithm, for the graph realization problem in ℝ3, given a sparse and noisy set of distance measurements. 3D-ASAP is a divide and conquer, non-incremental and non-iterative algorithm, which integrates local distance information into a global structure determination. Our approach starts with identifying, for every node, a subgraph of its 1-hop neighborhood graph, which can be accurately embedded in its own coordinate system. In the noise-free case, the computed coordinates of the sensors in each patch must agree with their global positioning up to some unknown rigid motion, that is, up to translation, rotation and possibly reflection. In other words, to every patch, there corresponds an element of the Euclidean group, Euc(3), of rigid transformations in ℝ3, and the goal was to estimate the group elements that will properly align all the patches in a globally consistent way. Furthermore, 3D-ASAP successfully incorporates information specific to the molecule problem in structural biology, in particular information on known substructures and their orientation. In addition, we also propose 3D-spectral-partitioning (SP)-ASAP, a faster version of 3D-ASAP, which uses a spectral partitioning algorithm as a pre-processing step for dividing the initial graph into smaller subgraphs. Our extensive numerical simulations show that 3D-ASAP and 3D-SP-ASAP are very robust to high levels of noise in the measured distances and to sparse connectivity in the measurement graph, and compare favorably with similar state-of-the-art localization algorithms.

Connected through hyperedge HE-1 (induced by source paper "an mbo scheme for clustering and semi supervised clustering of signed networks"):
  with members:
    - "node classification for signed networks using diffuse interface methods"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "a survey of signed network mining in social media"
    - "a method based on total variation for network modularity optimization using the mbo scheme"
    - "notes on elementary spectral graph theory applications to graph clustering using normalized cuts"
    - "extracting spatial information from networks with low order eigenvectors"
    - "max cut and the smallest eigenvalue"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "on uniquely registrable networks"):
  with members:
    - "large scale sensor network localization via rigid subnetwork registration"
    - "distributed maximum likelihood sensor network localization"
    - "global registration of multiple point clouds using semidefinite programming"
    - "on affine rigidity"

Connected through hyperedge HE-3 (induced by source paper "a survey of structure from motion"):
  with members:
    - "a survey on non filter based monocular visual slam systems"
    - "shapefit and shapekick for robust scalable structure from motion"
    - "shapefit exact location recovery from corrupted pairwise directions"
    - "exact simultaneous recovery of locations and structure from known orientations and corrupted point correspondences"
    - "robust inference for visual inertial sensor fusion"
    - "robust camera location estimation by convex programming"
    - "stable camera motion estimation using convex programming"
    - "exact and stable recovery of rotations for robust synchronization"

Connected through hyperedge HE-4 (induced by source paper "detection of core periphery structure in networks using spectral methods and geodesic paths"):
  with members:
    - "centrality metrics and localization in core periphery networks"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "identification of core periphery structure in networks"
    - "role discovery in networks"
    - "a local perspective on community structure in multilayer networks"
    - "core periphery structure in networks"
    - "social structure of facebook networks"
    - "structure and overlaps of communities in networks"

Connected through hyperedge HE-5 (induced by source paper "integrating noe and rdc using semidefinite programming for protein structure determination"):
  with members:
    - "manopt a matlab toolbox for optimization on manifolds"

Connected through hyperedge HE-6 (induced by source paper "integrating noe and rdc using sum of squares relaxation for protein structure determination"):
  with members:
    - "manopt a matlab toolbox for optimization on manifolds"

Connected through hyperedge HE-7 (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"
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "information recovery from pairwise measurements a shannon theoretic approach"):
  with members:
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "optimal haplotype assembly from high throughput mate pair reads"
    - "near optimal joint object matching via convex relaxation"
    - "minimax estimation of functionals of discrete distributions"
    - "tight error bounds for structured prediction"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "renyi divergence and kullback leibler divergence"
    - "exact recovery in the 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.CE(Computational Engineering, Finance, and Science)