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: "learning communities in the presence of errors"
  Abstract: We study the problem of learning communities in the presence of modeling errors and give robust recovery algorithms for the Stochastic Block Model (SBM). This model, which is also known as the Planted Partition Model, is widely used for community detection and graph partitioning in various fields, including machine learning, statistics, and social sciences. Many algorithms exist for learning communities in the Stochastic Block Model, but they do not work well in the presence of errors. #R##N#In this paper, we initiate the study of robust algorithms for partial recovery in SBM with modeling errors or noise. We consider graphs generated according to the Stochastic Block Model and then modified by an adversary. We allow two types of adversarial errors, Feige---Kilian or monotone errors, and edge outlier errors. Mossel, Neeman and Sly (STOC 2015) posed an open question about whether an almost exact recovery is possible when the adversary is allowed to add $o(n)$ edges. Our work answers this question affirmatively even in the case of $k>2$ communities. #R##N#We then show that our algorithms work not only when the instances come from SBM, but also work when the instances come from any distribution of graphs that is $\epsilon m$ close to SBM in the Kullback---Leibler divergence. This result also works in the presence of adversarial errors. Finally, we present almost tight lower bounds for two communities.

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"
    - "testing cluster structure of graphs"
    - "how robust are reconstruction thresholds for community detection"
    - "correlation clustering with noisy partial information"
    - "partitioning well clustered graphs spectral clustering works"

Connected through hyperedge HE-2 (induced by source paper "planted models for k way edge and vertex expansion"):
  with members:
    - "semi random graphs with planted sparse vertex cuts algorithms for exact and approximate recovery"
    - "multisection in the stochastic block model using semidefinite programming"
    - "how robust are reconstruction thresholds for community detection"
    - "partitioning well clustered graphs spectral clustering works"
    - "constant factor approximation for balanced cut in the pie model"
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "exact recovery in the stochastic block model"
    - "the complexity of approximating vertex expansion"

Connected through hyperedge HE-3 (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-4 (induced by source paper "robustness of spectral methods for community detection"):
  with members:
    - "graph powering and spectral robustness"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "how robust are reconstruction thresholds for community detection"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"

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 mixtures of gaussians in high dimensions"
    - "how robust are reconstruction thresholds for community detection"
    - "the hardness of approximation of euclidean k means"
    - "correlation clustering with noisy partial information"
    - "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 "a semidefinite program for unbalanced multisection in the stochastic block model"):
  with members:
    - "multisection in the stochastic block model using semidefinite programming"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "how robust are reconstruction thresholds for community detection"
    - "semidefinite programs on sparse random graphs"
    - "on semidefinite relaxations for the block model"
    - "exact recovery in the stochastic block model"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-7 (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"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "correlation clustering with noisy partial information"
    - "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)