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: "multisection in the stochastic block model using semidefinite programming"
  Abstract: We consider the problem of identifying underlying community-like structures in graphs. Toward this end, we study the stochastic block model (SBM) on k-clusters: a random model on n = km vertices, partitioned in k equal sized clusters, with edges sampled independently across clusters with probability q and within clusters with probability p, p > q. The goal is to recover the initial “hidden” partition of [n]. We study semidefinite programming (SDP)-based algorithms in this context. In the regime \(p = \frac {\alpha \log (m)}{m}\) and \(q = \frac {\beta \log (m)}{m}\), we show that a certain natural SDP-based algorithm solves the problem of exact recovery in the k-community SBM, with high probability, whenever \(\sqrt {\alpha } - \sqrt {\beta } > \sqrt {1}\), as long as \(k=o(\log n)\). This threshold is known to be the information theoretically optimal. We also study the case when \(k=\theta (\log (n))\). In this case however, we achieve recovery guarantees that no longer match the optimal condition \(\sqrt {\alpha } - \sqrt {\beta } > \sqrt {1}\), thus leaving achieving optimality for this range an open question.

Connected through hyperedge HE-1 (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"
    - "learning communities in the presence of errors"
    - "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-2 (induced by source paper "searching for a single community in a graph"):
  with members:
    - "estimating the number of communities in a network"
    - "accurate community detection in the stochastic block model via spectral algorithms"
    - "exact recovery in the stochastic block model"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "pseudo likelihood methods for community detection in large sparse networks"
    - "fast detection of overlapping communities via online tensor methods"
    - "breaking the small cluster barrier of graph clustering"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-3 (induced by source paper "semi random graphs with planted sparse vertex cuts algorithms for exact and approximate recovery"):
  with members:
    - "community detection in hypergraphs spiked tensor models and sum of squares"
    - "how robust are reconstruction thresholds for community detection"

Connected through hyperedge HE-4 (induced by source paper "does robustness imply tractability a lower bound for planted clique in the semi random model"):
  with members:
    - "a new algorithm for the robust semi random independent set problem"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "learning from untrusted data"
    - "how robust are reconstruction thresholds for community detection"

Connected through hyperedge HE-5 (induced by source paper "learning from untrusted data"):
  with members:
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "avoiding imposters and delinquents adversarial crowdsourcing and peer prediction"
    - "robust regression via hard thresholding"
    - "how robust are reconstruction thresholds for community detection"
    - "the power of localization for efficiently learning linear separators with noise"
    - "improved spectral norm bounds for clustering"
    - "algorithms and hardness for robust subspace recovery"

Connected through hyperedge HE-6 (induced by source paper "avoiding imposters and delinquents adversarial crowdsourcing and peer prediction"):
  with members:
    - "robust collaborative online learning"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "truth serums for massively crowdsourced evaluation tasks"
    - "how robust are reconstruction thresholds for community detection"
    - "approval voting and incentives in crowdsourcing"
    - "provably manipulation resistant reputation systems"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-7 (induced by source paper "a representation theory perspective on simultaneous alignment and classification"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "heterogeneous multireference alignment a single pass approach"
    - "continuously heterogeneous hyper objects in cryo em and 3 d movies of many temporal dimensions"
    - "a max cut approach to heterogeneity in cryo electron microscopy"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "covariance estimation using conjugate gradient for 3d classification in cryo em"
    - "multireference alignment using semidefinite programming"

Connected through hyperedge HE-8 (induced by source paper "learning communities in the presence of errors"):
  with members:
    - "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"
    - "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"

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)