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: "lp based robust algorithms for noisy minor free and bounded treewidth graphs"
  Abstract: We give a general approach for solving optimization problems on noisy minor free graphs, where a \delta-fraction of edges and vertices are adversarially corrupted. The noisy setting was first considered by Magen and Moharrami and they gave a (1 + \delta)-estimation algorithm for the independent set problem. Later, Chan and Har-Peled designed a local search algorithm that finds a (1 + O(\delta))-approximate independent set. However, nothing was known regarding other problems in the noisy setting. Our main contribution is a general LP-based framework that yields a (1 + O(\delta log m log log m))-approximation algorithm for noisy MAX-k-CSPs on m clauses.

Connected through hyperedge HE-1 (induced by source paper "covering vectors by spaces in perturbed graphic matroids and their duals"):
  with members:
    - "sparse recovery over graph incidence matrices"
    - "covering vectors by spaces regular matroids"
    - "fast robust pca on graphs"
    - "fixed parameter approximability of boolean mincsps"
    - "designing fpt algorithms for cut problems using randomized contractions"
    - "robust principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "bilu linial stability certified algorithms and the independent set problem"):
  with members:
    - "on euclidean k means clustering with alpha center proximity"
    - "exact algorithms and lower bounds for stable instances of euclidean k means"
    - "perturbation resilient clustering for k center and related problems via lp relaxations"
    - "stability and recovery for independence systems"
    - "k center clustering under perturbation resilience"
    - "on the lov asz theta function for independent sets in sparse graphs"
    - "sum of squares lower bounds for planted clique"
    - "bilu linial stable instances of max cut and minimum multiway cut"

Connected through hyperedge HE-3 (induced by source paper "losing treewidth by separating subsets"):
  with members:
    - "linear kernels for edge deletion problems to immersion closed graph classes"
    - "parameterized vertex deletion problems for hereditary graph classes with a block property"
    - "approximation and kernelization for chordal vertex deletion"
    - "a single exponential fixed parameter algorithm for distance hereditary vertex deletion"
    - "partitioning a graph into small pieces with applications to path transversal"
    - "on the computational complexity of vertex integrity and component order connectivity"
    - "polynomial bounds for the grid minor theorem"
    - "fixed parameter tractability of multicut parameterized by the size of the cutset"

Connected through hyperedge HE-4 (induced by source paper "polylogarithmic approximation algorithms for weighted mathcal f deletion problems"):
  with members:
    - "feedback vertex set inspired kernel for chordal vertex deletion"
    - "approximation and kernelization for chordal vertex deletion"
    - "bidimensionality and kernels"
    - "bidimensionality and geometric graphs"
    - "hitting forbidden minors approximation and kernelization"
    - "bidimensionality and eptas"
    - "hitting diamonds and growing cacti"

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)