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: "faster and simpler width independent parallel algorithms for positive semidefinite programming"
  Abstract: This paper studies the problem of finding an $(1+\epsilon)$-approximate solution to positive semidefinite programs. These are semidefinite programs in which all matrices in the constraints and objective are positive semidefinite and all scalars are non-negative. #R##N#We present a simpler \NC parallel algorithm that on input with $n$ constraint matrices, requires $O(\frac{1}{\epsilon^3} log^3 n)$ iterations, each of which involves only simple matrix operations and computing the trace of the product of a matrix exponential and a positive semidefinite matrix. Further, given a positive SDP in a factorized form, the total work of our algorithm is nearly-linear in the number of non-zero entries in the factorization.

Connected through hyperedge HE-1 (induced by source paper "faster algorithms for high dimensional robust covariance estimation"):
  with members:
    - "high dimensional robust sparse regression"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "high dimensional robust mean estimation in nearly linear time"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "robustly learning a gaussian getting optimal error efficiently"

Connected through hyperedge HE-2 (induced by source paper "a rank 1 sketch for matrix multiplicative weights"):
  with members:
    - "introduction to online convex optimization"
    - "efficient structured matrix recovery and nearly linear time algorithms for solving inverse symmetric m matrices"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "stability of the lanczos method for matrix function approximation"
    - "geometric median in nearly linear time"

Connected through hyperedge HE-3 (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"
    - "a semidefinite program for unbalanced multisection in the stochastic block model"
    - "deep learning in neural networks"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "how robust are reconstruction thresholds for community detection"

Connected through hyperedge HE-4 (induced by source paper "high dimensional robust mean estimation in nearly linear time"):
  with members:
    - "high dimensional robust sparse regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "mixture models robustness and sum of squares proofs"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "learning from untrusted data"

Connected through hyperedge HE-5 (induced by source paper "a parallel approximation algorithm for mixed packing and covering semidefinite programs"):
  with members:
    - "a parallel approximation algorithm for positive semidefinite programming"
    - "two message quantum interactive proofs are in pspace"
    - "sequential and parallel algorithms for mixed packing and covering"

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)