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: "a semidefinite program for unbalanced multisection in the stochastic block model"
  Abstract: We propose a semidefinite programming (SDP) algorithm for community detection in the stochastic block model, a popular model for networks with latent community structure. We prove that our algorithm achieves exact recovery of the latent communities, up to the information-theoretic limits determined by Abbe and Sandon (2015). Our result extends prior SDP approaches by allowing for many communities of different sizes. By virtue of a semidefinite approach, our algorithms succeed against a semirandom variant of the stochastic block model, guaranteeing a form of robustness and generalization. We further explore how semirandom models can lend insight into both the strengths and limitations of SDPs in this setting.

Connected through hyperedge HE-1 (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"
    - "learning communities in the presence of errors"

Connected through hyperedge HE-2 (induced by source paper "non convex matrix completion against a semi random adversary"):
  with members:
    - "how to escape saddle points efficiently"
    - "matrix completion has no spurious local minimum"
    - "recovery guarantee of weighted low rank approximation via alternating minimization"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "deep learning in neural networks"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "how robust are reconstruction thresholds for community detection"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

Connected through hyperedge HE-3 (induced by source paper "joint alignment from pairwise differences with a noisy oracle"):
  with members:
    - "optimal learning of joint alignments with a faulty oracle"
    - "predicting positive and negative links with noisy queries theory practice"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "clustering via crowdsourcing"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "mathematical and algorithmic analysis of network and biological data"
    - "exact recovery in the stochastic block model"
    - "a tail bound for read k families of functions"

Connected through hyperedge HE-4 (induced by source paper "hidden hamiltonian cycle recovery via linear programming"):
  with members:
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "sum of squares lower bounds for planted clique"
    - "exact recovery in the stochastic block model"
    - "information theory of dna shotgun sequencing"
    - "distributed user profiling via spectral methods"
    - "belief propagation for weighted b matchings on arbitrary graphs and its relation to linear programs with integer solutions"

Connected through hyperedge HE-5 (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"
    - "how robust are reconstruction thresholds for community detection"
    - "semidefinite programs on sparse random graphs"
    - "correlation clustering with noisy partial information"
    - "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-6 (induced by source paper "how robust are reconstruction thresholds for community detection"):
  with members:
    - "learning communities in the presence of errors"
    - "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)