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: "density friendly graph decomposition"
  Abstract: Decomposing a graph into a hierarchical structure via k-core analysis is a standard operation in any modern graph-mining toolkit. k-core decomposition is a simple and efficient method that allows to analyze a graph beyond its mere degree distribution. More specifically, it is used to identify areas in the graph of increasing centrality and connectedness, and it allows to reveal the structural organization of the graph.   Despite the fact that k-core analysis relies on vertex degrees, k-cores do not satisfy a certain, rather natural, density property. Simply put, the most central k-core is not necessarily the densest subgraph. This inconsistency between k-cores and graph density provides the basis of our study.   We start by defining what it means for a subgraph to be locally-dense, and we show that our definition entails a nested chain decomposition of the graph, similar to the one given by k-cores, but in this case the components are arranged in order of increasing density. We show that such a locally-dense decomposition for a graph G = (V, E) can be computed in polynomial time. The running time of the exact decomposition algorithm is O(|V|^2|E|) but is significantly faster in practice. In addition, we develop a linear-time algorithm that provides a factor-2 approximation to the optimal locally-dense decomposition. Furthermore, we show that the k-core decomposition is also a factor-2 approximation, however, as demonstrated by our experimental evaluation, in practice k-cores have different structure than locally-dense subgraphs, and as predicted by the theory, k-cores are not always well-aligned with graph density.

Connected through hyperedge HE-1 (induced by source paper "computing the k densest subgraphs of a graph"):
  with members:
    - "top k overlapping densest subgraphs approximation and complexity"
    - "densest subgraph in streaming and mapreduce"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-2 (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"
    - "densest subgraph in dynamic graph streams"
    - "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-3 (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"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "mathematical and algorithmic analysis of network and biological data"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"

Connected through hyperedge HE-4 (induced by source paper "distance generalized core decomposition"):
  with members:
    - "mining maximal span cores from temporal networks"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "the minimum wiener connector problem"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "social resilience in online communities the autopsy of friendster"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "truss decomposition in massive networks"
    - "four degrees of separation"

Connected through hyperedge HE-5 (induced by source paper "top k densest subgraphs in sliding window graph streams"):
  with members:
    - "a fast order based approach for core maintenance"
    - "densest subgraph in dynamic graph streams"
    - "when two choices are not enough balancing at scale in distributed stream processing"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "densest subgraph in streaming and mapreduce"
    - "defining and evaluating network communities based on ground truth"

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)