human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "computational hardness of certifying bounds on constrained pca problems"):
  Abstract: Given a random $n \times n$ symmetric matrix $\boldsymbol W$ drawn from the Gaussian orthogonal ensemble (GOE), we consider the problem of certifying an upper bound on the maximum value of the quadratic form $\boldsymbol x^\top \boldsymbol W \boldsymbol x$ over all vectors $\boldsymbol x$ in a constraint set $\mathcal{S} \subset \mathbb{R}^n$. For a certain class of normalized constraint sets $\mathcal{S}$, we give strong evidence that there is no polynomial-time algorithm certifying a better upper bound than the largest eigenvalue of $\boldsymbol W$. A notable special case included in our results is the hypercube $\mathcal{S} = \{ \pm 1 / \sqrt{n}\}^n$, which corresponds to the problem of certifying bounds on the Hamiltonian of the Sherrington-Kirkpatrick spin glass model from statistical physics. #R##N#Our proof proceeds in two steps. First, we give a reduction from the detection problem in the negatively-spiked Wishart model to the above certification problem. We then give evidence that this Wishart detection problem is computationally hard below the classical spectral threshold, using a method of Hopkins and Steurer based on approximating the likelihood ratio with a low-degree polynomial. Our proof can be seen as constructing a distribution over symmetric matrices that appears computationally indistinguishable from the GOE, yet is supported on matrices whose maximum quadratic form over $\boldsymbol x \in \mathcal{S}$ is much larger than that of a GOE matrix.

Includes members:
  Member "mean field approximation convex hierarchies and the optimality of correlation rounding a unified perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast convergence of belief propagation to global optima beyond correlation decay")
  Member "heterogeneous multireference alignment a single pass approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "harmonic alignment")
  Member "community detection in hypergraphs spiked tensor models and sum of squares":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exact recovery for a family of community detection generative models")
  Member "the lov asz theta function for random regular graphs and community detection in the hard regime":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local statistics semidefinite programming and community detection")
  Member "the power of sum of squares for detecting hidden structures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries")
  Member "bayesian estimation from few samples community detection and related problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "list decodable subspace recovery")
  Member "sum of squares lower bounds for refuting any csp":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sum of squares meets square loss fast rates for agnostic tensor completion")
  Member "a nearly tight sum of squares lower bound for the planted clique problem":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "broadcast congested clique planted cliques and pseudorandom generators")

Question: Please tell me which class the source paper 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)