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: "mathematical and algorithmic analysis of network and biological data"
  Abstract: This dissertation contributes to mathematical and algorithmic problems that arise in the analysis of network and biological data.

Connected through hyperedge HE-1 (induced by source paper "efficient algorithms for densest subgraph discovery"):
  with members:
    - "exploring communities in large profiled graphs"
    - "a survey of community search over big graphs"
    - "local algorithms for hierarchical dense subgraph discovery"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "the ubiquity of large graphs and surprising challenges of graph processing"
    - "density friendly graph decomposition"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"

Connected through hyperedge HE-2 (induced by source paper "joint alignment from pairwise differences with a noisy oracle"):
  with members:
    - "optimal learning of joint alignments with a faulty oracle"
    - "predicting positive and negative links with noisy queries theory practice"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "clustering via crowdsourcing"
    - "a semidefinite program for unbalanced multisection in the stochastic block model"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "exact recovery in the stochastic block model"
    - "a tail bound for read k families of functions"

Connected through hyperedge HE-3 (induced by source paper "predicting positive and negative links with noisy queries theory practice"):
  with members:
    - "scalable motif aware graph clustering"
    - "clustering via crowdsourcing"
    - "predicting signed edges with o n 1 o 1 log n queries"
    - "a new correlation clustering method for cancer mutation analysis"
    - "correlation clustering with noisy partial information"
    - "streaming graph partitioning in the planted partition model"
    - "analysis of boolean functions"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-4 (induced by source paper "a novel approach to finding near cliques the triangle densest subgraph problem"):
  with members:
    - "counting small cliques in mapreduce"
    - "a second look at counting triangles in graph streams revised"
    - "decompositions of triangle dense graphs"
    - "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"

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)