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: "reducibility and computational lower bounds for problems with planted sparse structure"
  Abstract: The prototypical high-dimensional statistics problem entails finding a structured signal in noise. Many of these problems exhibit an intriguing phenomenon: the amount of data needed by all known computationally efficient algorithms far exceeds what is needed for inefficient algorithms that search over all possible structures. A line of work initiated by Berthet and Rigollet in 2013 has aimed to explain these statistical-computational gaps by reducing from conjecturally hard average-case problems in computer science. However, the delicate nature of average-case reductions has limited the applicability of this approach. In this work we introduce several new techniques to give a web of average-case reductions showing strong computational lower bounds based on the planted clique conjecture using natural problems as intermediates. These include tight lower bounds for Planted Independent Set, Planted Dense Subgraph, Sparse Spiked Wigner, Sparse PCA, a subgraph variant of the Stochastic Block Model and a biased variant of Sparse PCA. We also give algorithms matching our lower bounds and identify the information-theoretic limits of the models we consider.

Connected through hyperedge HE-1 (induced by source paper "adversarially robust low dimensional representations"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "how hard is robust mean estimation"
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "on robustness to adversarial examples and polynomial optimization"
    - "approximating operator norms via generalized krivine rounding"
    - "a spectral view of adversarially robust features"
    - "high dimensional robust mean estimation in nearly linear time"
    - "high accuracy low precision training"

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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "accuracy memory tradeoffs and phase transitions in belief propagation"):
  with members:
    - "the circuit complexity of inference"
    - "communication complexity of estimating correlations"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "extractor based time space lower bounds for learning"
    - "secret key generation with limited interaction"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "information theoretic lower bounds for distributed function computation"

Connected through hyperedge HE-4 (induced by source paper "0 1 phase transitions in sparse spiked matrix estimation"):
  with members:
    - "mutual information for low rank even order symmetric tensor factorization"
    - "finite sample analysis of approximate message passing algorithms"
    - "estimation in the spiked wigner model a short proof of the replica formula"
    - "the layered structure of tensor estimation and its mutual information"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "sum of squares lower bounds for sparse pca"
    - "information theoretically optimal sparse pca"

Connected through hyperedge HE-5 (induced by source paper "learning some popular gaussian graphical models without condition number bounds"):
  with members:
    - "learning graphical models using multiplicative weights"
    - "information theoretic optimal learning of gaussian graphical models"
    - "interaction screening efficient and sample optimal learning of ising models"
    - "learning without concentration"
    - "efficiently learning ising models on arbitrary graphs"
    - "towards a mathematical theory of super resolution"
    - "subset selection for gaussian markov random fields"

Connected through hyperedge HE-6 (induced by source paper "balancing gaussian vectors in high dimension"):
  with members:
    - "on the discrepancy of random low degree set systems"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "number balancing is as hard as minkowski s theorem and shortest vector"
    - "constructive discrepancy minimization for convex sets"
    - "constructive algorithms for discrepancy minimization"
    - "analysis of the karmarkar karp differencing algorithm"

Connected through hyperedge HE-7 (induced by source paper "optimal average case reductions to sparse pca from weak assumptions to strong hardness"):
  with members:
    - "on finding dense common subgraphs"
    - "the power of sum of squares for detecting hidden structures"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "incoherence optimal matrix completion"
    - "label optimal regret bounds for online local learning"
    - "random tensors and planted cliques"

Connected through hyperedge HE-8 (induced by source paper "the average case complexity of counting cliques in erdos renyi hypergraphs"):
  with members:
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "on independent sets in random graphs"
    - "incoherence optimal matrix completion"
    - "hidden cliques and the certification of the restricted isometry property"
    - "nuclear norm minimization for the planted clique and biclique problems"

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)