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: "tight lower bounds for planted clique in the degree 4 sos program"
  Abstract: We give a lower bound of $\tilde{\Omega}(\sqrt{n})$ for the degree-4 Sum-of-Squares SDP relaxation for the planted clique problem. Specifically, we show that on an Erd\"os-R\'enyi graph $G(n,\tfrac{1}{2})$, with high probability there is a feasible point for the degree-4 SOS relaxation of the clique problem with an objective value of $\tilde{\Omega}(\sqrt{n})$, so that the program cannot distinguish between a random graph and a random graph with a planted clique of size $\tilde{O}(\sqrt{n})$. This bound is tight. #R##N#We build on the works of Deshpande and Montanari and Meka et al., who give lower bounds of $\tilde{\Omega}(n^{1/3})$ and $\tilde{\Omega}(n^{1/4})$ respectively. We improve on their results by making a perturbation to the SDP solution proposed in their work, then showing that this perturbation remains PSD as the objective value approaches $\tilde{\Omega}(n^{1/2})$. #R##N#In an independent work, Hopkins, Kothari and Potechin [HKP15] have obtained a similar lower bound for the degree-$4$ SOS relaxation.

Connected through hyperedge HE-1 (induced by source paper "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries"):
  with members:
    - "how hard is robust mean estimation"
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "computational hardness of certifying bounds on constrained pca problems"
    - "efficient algorithms for outlier robust regression"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "robust sparse estimation tasks in high dimensions"
    - "the power of sum of squares for detecting hidden structures"
    - "bayesian estimation from few samples community detection and related problems"

Connected through hyperedge HE-2 (induced by source paper "reducibility and computational lower bounds for problems with planted sparse structure"):
  with members:
    - "sherali adams integrality gaps matching the log density threshold"
    - "on finding dense common subgraphs"
    - "robust sparse estimation tasks in high dimensions"
    - "the power of sum of squares for detecting hidden structures"
    - "bayesian estimation from few samples community detection and related problems"
    - "sum of squares lower bounds for refuting any csp"
    - "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"

Connected through hyperedge HE-3 (induced by source paper "the power of sum of squares for detecting hidden structures"):
  with members:
    - "exact tensor completion with sum of squares"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "polynomial time tensor decompositions with sum of squares"
    - "multiplicative approximations for polynomial optimization over the unit sphere"
    - "how well do local algorithms solve semidefinite programs"
    - "spectral algorithms for tensor completion"
    - "strongly refuting random csps below the spectral threshold"
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"

Connected through hyperedge HE-4 (induced by source paper "tensor prediction rademacher complexity and random 3 xor"):
  with members:
    - "how to refute a random csp"
    - "sos and planted clique tight analysis of mpw moments at all degrees and an optimal lower bound at degree four"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "rounding sum of squares relaxations"
    - "smoothed analysis of tensor decompositions"
    - "from average case complexity to improper learning complexity"
    - "understanding alternating minimization for matrix completion"

Connected through hyperedge HE-5 (induced by source paper "sos and planted clique tight analysis of mpw moments at all degrees and an optimal lower bound at degree four"):
  with members:
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "sum of squares lower bounds for sparse pca"
    - "sum of squares lower bounds from pairwise independence"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "sum of squares lower bounds for planted clique"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "sum of squares proofs and the quest toward optimal algorithms"

Connected through hyperedge HE-6 (induced by source paper "sum of squares lower bounds for sparse pca"):
  with members:
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "sos and planted clique tight analysis of mpw moments at all degrees and an optimal lower bound at degree four"
    - "sum of squares lower bounds for planted clique"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "rounding sum of squares relaxations"
    - "more data speeds up training time in learning halfspaces over sparse vectors"

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)