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: "sum of squares lower bounds for refuting any csp"
  Abstract: Let $P:\{0,1\}^k \to \{0,1\}$ be a nontrivial $k$-ary predicate. Consider a random instance of the constraint satisfaction problem $\mathrm{CSP}(P)$ on $n$ variables with $\Delta n$ constraints, each being $P$ applied to $k$ randomly chosen literals. Provided the constraint density satisfies $\Delta \gg 1$, such an instance is unsatisfiable with high probability. The \emph{refutation} problem is to efficiently find a proof of unsatisfiability. #R##N#We show that whenever the predicate $P$ supports a $t$-\emph{wise uniform} probability distribution on its satisfying assignments, the sum of squares (SOS) algorithm of degree $d = \Theta(\frac{n}{\Delta^{2/(t-1)} \log \Delta})$ (which runs in time $n^{O(d)}$) \emph{cannot} refute a random instance of $\mathrm{CSP}(P)$. In particular, the polynomial-time SOS algorithm requires $\widetilde{\Omega}(n^{(t+1)/2})$ constraints to refute random instances of CSP$(P)$ when $P$ supports a $t$-wise uniform distribution on its satisfying assignments. Together with recent work of Lee et al. [LRS15], our result also implies that \emph{any} polynomial-size semidefinite programming relaxation for refutation requires at least $\widetilde{\Omega}(n^{(t+1)/2})$ constraints. #R##N#Our results (which also extend with no change to CSPs over larger alphabets) subsume all previously known lower bounds for semialgebraic refutation of random CSPs. For every constraint predicate~$P$, they give a three-way hardness tradeoff between the density of constraints, the SOS degree (hence running time), and the strength of the refutation. By recent algorithmic results of Allen et al. [AOW15] and Raghavendra et al. [RRS16], this full three-way tradeoff is \emph{tight}, up to lower-order factors.

Connected through hyperedge HE-1 (induced by source paper "sum of squares meets square loss fast rates for agnostic tensor completion"):
  with members:
    - "efficient algorithms for outlier robust regression"
    - "exact tensor completion with sum of squares"
    - "spectral algorithms for tensor completion"
    - "strongly refuting random csps below the spectral threshold"
    - "complexity theoretic limitations on learning halfspaces"
    - "learning without concentration"
    - "nuclear norm of higher order tensors"
    - "a new convex relaxation for tensor completion"

Connected through hyperedge HE-2 (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-3 (induced by source paper "computational hardness of certifying bounds on constrained pca problems"):
  with members:
    - "mean field approximation convex hierarchies and the optimality of correlation rounding a unified perspective"
    - "heterogeneous multireference alignment a single pass approach"
    - "community detection in hypergraphs spiked tensor models and sum of squares"
    - "the lov asz theta function for random regular graphs and community detection in the hard regime"
    - "the power of sum of squares for detecting hidden structures"
    - "bayesian estimation from few samples community detection and related problems"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "mutual information in rank one matrix estimation"

Connected through hyperedge HE-4 (induced by source paper "extended formulation lower bounds for refuting random csps"):
  with members:
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "approximating rectangles by juntas and weakly exponential lower bounds for lp relaxations of csps"
    - "strongly refuting random csps below the spectral threshold"
    - "how to refute a random csp"
    - "complexity theoretic limitations on learning halfspaces"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "analysis of boolean functions"

Connected through hyperedge HE-5 (induced by source paper "approximating constraint satisfaction problems on high dimensional expanders"):
  with members:
    - "agreement testing theorems on layered set systems"
    - "good distance lattices from high dimensional expanders"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "high dimensional random walks and colorful expansion"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "a new regularity lemma and faster approximation algorithms for low threshold rank graphs"
    - "faster sdp hierarchy solvers for local rounding algorithms"
    - "rounding semidefinite programming hierarchies via global correlation"

Connected through hyperedge HE-6 (induced by source paper "the sdp value for random two eigenvalue csps"):
  with members:
    - "the threshold for sdp refutation of random regular nae 3sat"
    - "the lov asz theta function for random regular graphs and community detection in the hard regime"
    - "the backtracking survey propagation algorithm for solving random k sat problems"
    - "strongly refuting random csps below the spectral threshold"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "a composition theorem for parity kill number"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-7 (induced by source paper "lifting sum of squares lower bounds degree 2 to degree 4"):
  with members:
    - "extended formulation lower bounds for refuting random csps"
    - "the threshold for sdp refutation of random regular nae 3sat"
    - "the power of sum of squares for detecting hidden structures"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "strongly refuting random csps below the spectral threshold"
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "semidefinite programs on sparse random graphs and their application to community detection"

Connected through hyperedge HE-8 (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"
    - "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"
    - "minimizing the union tight approximations for small set bipartite vertex expansion"

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.CC(Computational Complexity)