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: "near optimal fully dynamic densest subgraph"
  Abstract: We give the first fully dynamic algorithm which maintains a $(1+\epsilon)$-approximate densest subgraph in worst-case time $\text{poly}(\log n, \epsilon^{-1})$ per update with high probability. Dense subgraph discovery is an important primitive for many real-world applications such as community detection, link spam detection, distance query indexing, and computational biology. Our result improves upon the previous best approximation factor of $(4+\epsilon)$ for fully dynamic densest subgraph obtained by [Bhattacharya-Henzinger-Nanongkai-Tsourakakis, STOC`15]. Our algorithm combines the uniform sparsification technique used in [Mitzenmacher-Pachocki-Peng-Tsourakakis-Xu, KDD`15] and [McGregor-Tench-Vorotnikova-Vu, MFCS`15] along with an augmenting path-like dual adjustment technique to maintain an approximate solution efficiently.

Connected through hyperedge HE-1 (induced by source paper "distributed dense subgraph detection and low outdegree orientation"):
  with members:
    - "improved distributed expander decomposition and nearly optimal triangle enumeration"
    - "distributed approximation algorithms for maximum matching in graphs and hypergraphs"
    - "improved deterministic distributed matching via rounding"
    - "improved distributed degree splitting and edge coloring"
    - "on derandomizing local distributed algorithms"
    - "distributed degree splitting edge coloring and orientations"
    - "densest subgraph in dynamic graph streams"
    - "applications of uniform sampling densest subgraph and beyond"

Connected through hyperedge HE-2 (induced by source paper "flowless extracting densest subgraphs without flow computations"):
  with members:
    - "twittermancer predicting interactions on twitter accurately"
    - "novel dense subgraph discovery primitives risk aversion and exclusion queries"
    - "parallel and streaming algorithms for k core decomposition"
    - "the densest subgraph problem with a convex concave size function"
    - "densest subgraph in dynamic graph streams"
    - "density friendly graph decomposition"
    - "applications of uniform sampling densest subgraph and beyond"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"

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)