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: "decompositions of triangle dense graphs"
  Abstract: High triangle density -- the graph property stating that a constant fraction of two-hop paths belong to a triangle -- is a common signature of social networks. This paper studies triangle-dense graphs from a structural perspective. We prove constructively that significant portions of a triangle-dense graph are contained in a disjoint union of dense, radius 2 subgraphs. This result quantifies the extent to which triangle-dense graphs resemble unions of cliques. We also show that our algorithm recovers planted clusterings in approximation-stable k-median instances.

Connected through hyperedge HE-1 (induced by source paper "mrattractor detecting communities from large scale graphs"):
  with members:
    - "distributed community detection with the wcc metric"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "robust communication optimal distributed clustering algorithms"):
  with members:
    - "perturbation resilient clustering for k center and related problems via lp relaxations"
    - "distributed partial clustering"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "communication optimal distributed clustering"
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "data driven resource allocation for distributed learning"
    - "k center clustering under perturbation resilience"

Connected through hyperedge HE-3 (induced by source paper "clustering under local stability bridging the gap between worst case and beyond worst case analysis"):
  with members:
    - "k center clustering under perturbation resilience"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "beyond worst case analysis in private singular vector computation"

Connected through hyperedge HE-4 (induced by source paper "k center clustering under perturbation resilience"):
  with members:
    - "improved and simplified inapproximability for k means"
    - "a bi criteria approximation algorithm for k means"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "bilu linial stable instances of max cut and minimum multiway cut"
    - "on the practically interesting instances of maxcut"
    - "improved spectral norm bounds for clustering"
    - "beyond worst case analysis in private singular vector computation"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-5 (induced by source paper "data driven resource allocation for distributed learning"):
  with members:
    - "an improved approximation algorithm for the hard uniform capacitated k median problem"
    - "improved distributed principal component analysis"
    - "approximation algorithms for hard capacitated k facility location problems"
    - "bi factor approximation algorithms for hard capacitated k median problems"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-6 (induced by source paper "symmetric and asymmetric k center clustering under stability"):
  with members:
    - "beyond worst case analysis in private singular vector computation"
    - "data stability in clustering a closer look"
    - "the complexity of planar counting problems"

Connected through hyperedge HE-7 (induced by source paper "a novel approach to finding near cliques the triangle densest subgraph problem"):
  with members:
    - "counting small cliques in mapreduce"
    - "mathematical and algorithmic analysis of network and biological data"
    - "a second look at counting triangles in graph streams revised"
    - "parallel peeling algorithms"
    - "how hard is counting triangles in the streaming model"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "densest subgraph in streaming and mapreduce"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

Connected through hyperedge HE-8 (induced by source paper "a scalable generative graph model with community structure"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "an in depth analysis of stochastic kronecker graphs"
    - "triadic measures on graphs the power of wedge sampling"
    - "community structure and scale free collections of erdős renyi graphs"
    - "a scalable null model for directed graphs matching all degree distributions in out and reciprocal"
    - "revisiting degree distribution models for social graph analysis"
    - "the similarity between stochastic kronecker and chung lu graph models"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

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)