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: "correlation clustering with noisy partial information"
  Abstract: In this paper, we propose and study a semi-random model for the Correlation Clustering problem on arbitrary graphs G. We give two approximation algorithms for Correlation Clustering instances from this model. The first algorithm finds a solution of value $(1+ \delta) optcost + O_{\delta}(n\log^3 n)$ with high probability, where $optcost$ is the value of the optimal solution (for every $\delta > 0$). The second algorithm finds the ground truth clustering with an arbitrarily small classification error $\eta$ (under some additional assumptions on the instance).

Connected through hyperedge HE-1 (induced by source paper "robust clustering oracle and local reconstructor of cluster structure of graphs"):
  with members:
    - "testing graph clusterability algorithms and lower bounds"
    - "approximate correlation clustering using same cluster queries"
    - "approximate clustering with same cluster queries"
    - "clustering with same cluster queries"
    - "learning communities in the presence of errors"
    - "testing cluster structure of graphs"
    - "how robust are reconstruction thresholds for community detection"
    - "partitioning well clustered graphs spectral clustering works"

Connected through hyperedge HE-2 (induced by source paper "towards learning sparsely used dictionaries with arbitrary supports"):
  with members:
    - "outlier robust moment estimation via sum of squares"
    - "clustering semi random mixtures of gaussians"
    - "an improved analysis of the er spud dictionary learning algorithm"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "on the effect of randomness on planted 3 coloring models"
    - "simple efficient and neural algorithms for sparse coding"
    - "a semidefinite program for unbalanced multisection in the stochastic block model"

Connected through hyperedge HE-3 (induced by source paper "generalizing the hypergraph laplacian via a diffusion process with mediators"):
  with members:
    - "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"
    - "partitioning well clustered graphs spectral clustering works"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "approximation algorithm for sparsest k partitioning"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "many sparse cuts via higher eigenvalues"

Connected through hyperedge HE-4 (induced by source paper "predicting positive and negative links with noisy queries theory practice"):
  with members:
    - "scalable motif aware graph clustering"
    - "clustering via crowdsourcing"
    - "predicting signed edges with o n 1 o 1 log n queries"
    - "a new correlation clustering method for cancer mutation analysis"
    - "streaming graph partitioning in the planted partition model"
    - "analysis of boolean functions"
    - "mathematical and algorithmic analysis of network and biological data"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-5 (induced by source paper "clustering semi random mixtures of gaussians"):
  with members:
    - "learning from untrusted data"
    - "agnostic estimation of mean and covariance"
    - "learning communities in the presence of errors"
    - "learning mixtures of gaussians in high dimensions"
    - "how robust are reconstruction thresholds for community detection"
    - "the hardness of approximation of euclidean k means"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "fourier pca and robust tensor decomposition"

Connected through hyperedge HE-6 (induced by source paper "approximate correlation clustering using same cluster queries"):
  with members:
    - "approximate clustering with same cluster queries"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "clustering with same cluster queries"
    - "predicting signed edges with o n 1 o 1 log n queries"
    - "efficient clustering with limited distance information"
    - "local algorithms for interactive clustering"
    - "clustering under perturbation resilience"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-7 (induced by source paper "learning communities in the presence of errors"):
  with members:
    - "multisection in the stochastic block model using semidefinite programming"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "a semidefinite program for unbalanced multisection in the stochastic block model"
    - "how robust are reconstruction thresholds for community detection"
    - "semidefinite programs on sparse random graphs"
    - "exact recovery in the stochastic block model"
    - "community detection thresholds and the weak ramanujan property"
    - "improved spectral norm bounds for clustering"

Connected through hyperedge HE-8 (induced by source paper "how robust are reconstruction thresholds for community detection"):
  with members:
    - "a semidefinite program for unbalanced multisection in the stochastic block model"
    - "learning communities in the presence of errors"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "on semidefinite relaxations for the block model"
    - "exact recovery in the stochastic block model"
    - "community detection thresholds and the weak ramanujan property"

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.DS(Data Structures and Algorithms)