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: "densest subgraph in dynamic graph streams"
  Abstract: In this paper, we consider the problem of approximating the densest subgraph in the dynamic graph stream model. In this model of computation, the input graph is defined by an arbitrary sequence of edge insertions and deletions and the goal is to analyze properties of the resulting graph given memory that is sub-linear in the size of the stream. We present a single-pass algorithm that returns a $(1+\epsilon)$ approximation of the maximum density with high probability; the algorithm uses $O(\epsilon^{-2} n \polylog n)$ space, processes each stream update in $\polylog (n)$ time, and uses $\poly(n)$ post-processing time where $n$ is the number of nodes. The space used by our algorithm matches the lower bound of Bahmani et al.~(PVLDB 2012) up to a poly-logarithmic factor for constant $\epsilon$. The best existing results for this problem were established recently by Bhattacharya et al.~(STOC 2015). They presented a $(2+\epsilon)$ approximation algorithm using similar space and another algorithm that both processed each update and maintained a $(4+\epsilon)$ approximation of the current maximum density in $\polylog (n)$ time per-update.

Connected through hyperedge HE-1 (induced by source paper "near optimal fully dynamic densest subgraph"):
  with members:
    - "distributed dense subgraph detection and low outdegree orientation"
    - "new algorithms and lower bounds for all pairs max flow in undirected graphs"
    - "inapproximability of maximum biclique problems minimum k cut and densest at least k subgraph from the small set expansion hypothesis"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"
    - "egalitarian graph orientations"
    - "the ubiquity of large graphs and surprising challenges of graph processing"
    - "new deterministic approximation algorithms for fully dynamic matching"
    - "popular conjectures as a barrier for dynamic planar graph algorithms"

Connected through hyperedge HE-2 (induced by source paper "distributed dense subgraph detection and low outdegree orientation"):
  with members:
    - "near optimal fully dynamic densest subgraph"
    - "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"
    - "applications of uniform sampling densest subgraph and beyond"

Connected through hyperedge HE-3 (induced by source paper "graph coloring via degeneracy in streaming and other space conscious models"):
  with members:
    - "simple graph coloring algorithms for congested clique and massively parallel computation"
    - "greedy and local ratio algorithms in the mapreduce model"
    - "dynamic algorithms for graph coloring"
    - "dynamic graph coloring"
    - "simple and near optimal distributed coloring for sparse graphs"
    - "local conflict coloring"
    - "hypergraph two coloring in the streaming model"
    - "communication steps for parallel query processing"

Connected through hyperedge HE-4 (induced by source paper "flowless extracting densest subgraphs without flow computations"):
  with members:
    - "near optimal fully dynamic densest subgraph"
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "metric sublinear algorithms via linear sampling"):
  with members:
    - "sublinear algorithms for maxcut and correlation clustering"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "distributed coverage maximization via sketching"
    - "almost optimal streaming algorithms for coverage problems"
    - "towards tight bounds for the streaming set cover problem"
    - "tight bounds for linear sketches of approximate matchings"
    - "line large scale information network embedding"

Connected through hyperedge HE-6 (induced by source paper "sublinear algorithms for delta 1 vertex coloring"):
  with members:
    - "coloring in graph streams"
    - "brief announcement semi mapreduce meets congested clique"
    - "greedy and local ratio algorithms in the mapreduce model"
    - "independent sets in vertex arrival streams"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "simple round compression for parallel vertex cover"
    - "round compression for parallel matching algorithms"

Connected through hyperedge HE-7 (induced by source paper "finding events in temporal networks segmentation meets densest subgraph discovery"):
  with members:
    - "strongly polynomial efficient approximation scheme for segmentation"
    - "evidense a graph based method for finding unique high impact events with succinct keyword based descriptions"
    - "local community detection in dynamic networks"
    - "best friends forever bff finding lasting dense subgraphs"
    - "applications of uniform sampling densest subgraph and beyond"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "the bursty dynamics of the twitter information network"
    - "a novel approach to finding near cliques the triangle densest subgraph problem"

Connected through hyperedge HE-8 (induced by source paper "parallel and streaming algorithms for k core decomposition"):
  with members:
    - "new deterministic approximation algorithms for fully dynamic matching"
    - "snap a general purpose network analysis and graph mining library"
    - "applications of uniform sampling densest subgraph and beyond"
    - "parallel algorithms for geometric graph problems"
    - "densest subgraph in streaming and mapreduce"
    - "defining and evaluating network communities based on ground truth"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "k core decomposition a tool for the visualization of large scale 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)