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 "on the randomized complexity of minimizing a convex quadratic function"):
  Abstract: Minimizing a convex, quadratic objective is a fundamental problem in machine learning and optimization. In this work, we study prove information-theoretic, gradient query complexity lower bounds for minimizing convex quadratic functions, which, unlike prior works, apply even for randomized algorithms. Specifically, we construct a distribution over quadratic functions that witnesses lower bounds which match those known for deterministic algorithms, up to multiplicative constants. The distribution which witnesses our lower bound is in fact quite benign: it is both closed form, and derived from classical ensembles in random matrix theory. We believe that our construction constitutes a plausible "average case" setting, and thus provides compelling evidence that the worst case and average case complexity of convex-quadratic optimization are essentially identical.

Includes members:
  Member "tight query complexity lower bounds for pca via finite sample deformed wigner law":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "querying a matrix through matrix vector products")
  Member "estimation in the spiked wigner model a short proof of the replica formula":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overlap matrix concentration in optimal bayesian inference")
  Member "optimal lower bounds for universal relation samplers and finding duplicates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tight query complexity lower bounds for pca via finite sample deformed wigner law")
  Member "lifting randomized query complexity to randomized communication complexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "mutual information in rank one matrix estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overlap matrix concentration in optimal bayesian inference")
  Member "mmse of probabilistic low rank matrix estimation universality with respect to the output channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries")
  Member "fundamental limits of online and distributed algorithms for statistical learning and estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "lower bounds for adaptive sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "querying a matrix through matrix vector products")

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.LG(Machine Learning)