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: "everywhere sparse spanners via dense subgraphs"
  Abstract: The significant progress in constructing graph spanners that are sparse (small number of edges) or light (low total weight) has skipped spanners that are everywhere-sparse (small maximum degree). This disparity is in line with other network design problems, where the maximum-degree objective has been a notorious technical challenge. Our main result is for the Lowest Degree 2-Spanner (LD2S) problem, where the goal is to compute a 2-spanner of an input graph so as to minimize the maximum degree. We design a polynomial-time algorithm achieving approximation factor $\tilde O(\Delta^{3-2\sqrt{2}}) \approx \tilde O(\Delta^{0.172})$, where $\Delta$ is the maximum degree of the input graph. The previous $\tilde O(\Delta^{1/4})$ -approximation was proved nearly two decades ago by Kortsarz and Peleg [SODA 1994, SICOMP 1998]. #R##N#Our main conceptual contribution is to establish a formal connection between LD2S and a variant of the Densest k-Subgraph (DkS) problem. Specifically, we design for both problems strong relaxations based on the Sherali-Adams linear programming (LP) hierarchy, and show that "faithful" randomized rounding of the DkS-variant can be used to round LD2S solutions. Our notion of faithfulness intuitively means that all vertices and edges are chosen with probability proportional to their LP value, but the precise formulation is more subtle. #R##N#Unfortunately, the best algorithms known for DkS use the Lov\'asz-Schrijver LP hierarchy in a non-faithful way [Bhaskara, Charikar, Chlamtac, Feige, and Vijayaraghavan, STOC 2010]. Our main technical contribution is to overcome this shortcoming, while still matching the gap that arises in random graphs by planting a subgraph with same log-density.

Connected through hyperedge HE-1 (induced by source paper "lasserre integrality gaps for graph spanners and related problems"):
  with members:
    - "characterizing demand graphs for fixed parameter shallow light steiner network"
    - "approximating spanners and directed steiner forest upper and lower bounds"
    - "parallel metric tree embedding based on an algebraic view on moore bellman ford"
    - "label cover instances with large girth and the hardness of approximating basic k spanner"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "fault tolerant spanners better and simpler"
    - "directed steiner tree and the lasserre hierarchy"
    - "directed spanners via flow based linear programs"

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 "distributed spanner approximation"):
  with members:
    - "distributed approximation of minimum k edge connected spanning subgraphs"
    - "improved deterministic distributed construction of spanners"
    - "distributed property testing for subgraph freeness revisited"
    - "quadratic and near quadratic lower bounds for the congest model"
    - "on derandomizing local distributed algorithms"
    - "distributed distance bounded network design through distributed convex programming"
    - "near linear lower bounds for distributed distance computations even in sparse networks"
    - "efficient algorithms for constructing very sparse spanners and emulators"

Connected through hyperedge HE-4 (induced by source paper "low rank approximation in the presence of outliers"):
  with members:
    - "better agnostic clustering via relaxed tensor norms"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "mixture models robustness and sum of squares proofs"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "learning from untrusted data"
    - "fast algorithms for robust pca via gradient descent"
    - "agnostic estimation of mean and covariance"

Connected through hyperedge HE-5 (induced by source paper "almost polynomial ratio eth hardness of approximating densest k subgraph"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"
    - "inapproximability results for approximate nash equilibria"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "partitioning a graph into small pieces with applications to path transversal"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "eth hardness for densest k subgraph with perfect completeness"
    - "eth hardness for signaling in symmetric zero sum games"
    - "approximating dense max 2 csps"

Connected through hyperedge HE-6 (induced by source paper "the densest k subhypergraph problem"):
  with members:
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "the complexity of approximating vertex expansion"
    - "approximating semi matchings in streaming and in two party communication"
    - "approximation algorithms and hardness of the k route cut problem"
    - "small approximate pareto sets for bi objective shortest paths and other problems"
    - "dial a ride from k forest"

Connected through hyperedge HE-7 (induced by source paper "approximating spanners and directed steiner forest upper and lower bounds"):
  with members:
    - "the 4 3 additive spanner exponent is tight"
    - "label cover instances with large girth and the hardness of approximating basic k spanner"
    - "fault tolerant spanners better and simpler"
    - "transitive closure spanners"

Connected through hyperedge HE-8 (induced by source paper "minimizing the union tight approximations for small set bipartite vertex expansion"):
  with members:
    - "maximum quadratic assignment problem reduction from maximum label cover and lp based approximation algorithm"
    - "approximation algorithms for hypergraph small set expansion and small set 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.DS(Data Structures and Algorithms)