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 polynomial time algorithm for lossy population recovery"
  Abstract: We give a polynomial time algorithm for the lossy population recovery problem. In this problem, the goal is to approximately learn an unknown distribution on binary strings of length $n$ from lossy samples: for some parameter $\mu$ each coordinate of the sample is preserved with probability $\mu$ and otherwise is replaced by a `?'. The running time and number of samples needed for our algorithm is polynomial in $n$ and $1/\varepsilon$ for each fixed $\mu>0$. This improves on algorithm of Wigderson and Yehudayoff that runs in quasi-polynomial time for any $\mu > 0$ and the polynomial time algorithm of Dvir et al which was shown to work for $\mu \gtrapprox 0.30$ by Batman et al. In fact, our algorithm also works in the more general framework of Batman et al. in which there is no a priori bound on the size of the support of the distribution. The algorithm we analyze is implicit in previous work; our main contribution is to analyze the algorithm by showing (via linear programming duality and connections to complex analysis) that a certain matrix associated with the problem has a robust local inverse even though its condition number is exponentially small. A corollary of our result is the first polynomial time algorithm for learning DNFs in the restriction access model of Dvir et al.

Connected through hyperedge HE-1 (induced by source paper "efficient average case population recovery in the presence of insertions and deletions"):
  with members:
    - "beyond trace reconstruction population recovery from the deletion channel"
    - "trace reconstruction generalized and parameterized"
    - "sharp bounds for population recovery"
    - "average case reconstruction for the deletion channel subpolynomially many traces suffice"
    - "noisy population recovery in polynomial time"
    - "optimal mean based algorithms for trace reconstruction"

Connected through hyperedge HE-2 (induced by source paper "beyond trace reconstruction population recovery from the deletion channel"):
  with members:
    - "sharp bounds for population recovery"
    - "average case reconstruction for the deletion channel subpolynomially many traces suffice"
    - "noisy population recovery in polynomial time"
    - "optimal mean based algorithms for trace reconstruction"

Connected through hyperedge HE-3 (induced by source paper "sharp bounds for population recovery"):
  with members:
    - "noisy population recovery in polynomial time"

Connected through hyperedge HE-4 (induced by source paper "a size free clt for poisson multinomials and its applications"):
  with members:
    - "on the structure covering and learning of poisson multinomial distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "on the complexity of nash equilibria in anonymous games"
    - "new algorithms and lower bounds for monotonicity testing"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"
    - "on oblivious ptas s for nash equilibrium"
    - "discretized multinomial distributions and nash equilibria in anonymous games"

Connected through hyperedge HE-5 (induced by source paper "provable algorithms for inference in topic models"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "learning mixtures of gaussians in high dimensions"
    - "a tensor approach to learning mixed membership community models"
    - "provable bounds for learning some deep representations"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "settling the polynomial learnability of mixtures of gaussians"

Connected through hyperedge HE-6 (induced by source paper "noisy population recovery in polynomial time"):
  with members:
    - "analysis of boolean functions"

Connected through hyperedge HE-7 (induced by source paper "super resolution extremal functions and the condition number of vandermonde matrices"):
  with members:
    - "music for single snapshot spectral estimation stability and super resolution"
    - "smoothed analysis of tensor decompositions"
    - "fourier pca and robust tensor decomposition"
    - "support detection in super resolution"
    - "nearly optimal sparse fourier transform"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-8 (induced by source paper "pac quasi automatizability of resolution over restricted distributions"):
  with members:
    - "learning implicitly in reasoning in pac semantics"
    - "towards understanding and harnessing the potential of clause learning"

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)