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: "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
  Abstract: While in many graph mining applications it is crucial to handle a stream of updates efficiently in terms of {\em both} time and space, not much was known about achieving such type of algorithm. In this paper we study this issue for a problem which lies at the core of many graph mining applications called {\em densest subgraph problem}. We develop an algorithm that achieves time- and space-efficiency for this problem simultaneously. It is one of the first of its kind for graph problems to the best of our knowledge. #R##N#In a graph $G = (V, E)$, the "density" of a subgraph induced by a subset of nodes $S \subseteq V$ is defined as $|E(S)|/|S|$, where $E(S)$ is the set of edges in $E$ with both endpoints in $S$. In the densest subgraph problem, the goal is to find a subset of nodes that maximizes the density of the corresponding induced subgraph. For any $\epsilon>0$, we present a dynamic algorithm that, with high probability, maintains a $(4+\epsilon)$-approximation to the densest subgraph problem under a sequence of edge insertions and deletions in a graph with $n$ nodes. It uses $\tilde O(n)$ space, and has an amortized update time of $\tilde O(1)$ and a query time of $\tilde O(1)$. Here, $\tilde O$ hides a $O(\poly\log_{1+\epsilon} n)$ term. The approximation ratio can be improved to $(2+\epsilon)$ at the cost of increasing the query time to $\tilde O(n)$. It can be extended to a $(2+\epsilon)$-approximation sublinear-time algorithm and a distributed-streaming algorithm. Our algorithm is the first streaming algorithm that can maintain the densest subgraph in {\em one pass}. The previously best algorithm in this setting required $O(\log n)$ passes [Bahmani, Kumar and Vassilvitskii, VLDB'12]. The space required by our algorithm is tight up to a polylogarithmic factor.

Connected through hyperedge HE-1 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism"):
  with members:
    - "storing and querying large scale spatio temporal graphs with high throughput edge insertions"
    - "parallel batch dynamic graph connectivity"
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "dynamic algorithms for the massively parallel computation model"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"

Connected through hyperedge HE-2 (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-3 (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"
    - "densest subgraph in dynamic graph streams"

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"
    - "densest subgraph in dynamic graph streams"
    - "density friendly graph decomposition"
    - "applications of uniform sampling densest subgraph and beyond"

Connected through hyperedge HE-5 (induced by source paper "mining bursting communities in temporal graphs"):
  with members:
    - "distance generalized core decomposition"
    - "community discovery in dynamic networks a survey"
    - "finding events in temporal networks segmentation meets densest subgraph discovery"
    - "mining maximal span cores from temporal networks"
    - "real time discovery of dense clusters in highly dynamic graphs identifying real world events in highly dynamic environments"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-6 (induced by source paper "clustered hierarchical entropy scaling search of astronomical and biological data"):
  with members:
    - "entropy scaling search of massive biological data"

Connected through hyperedge HE-7 (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"
    - "densest subgraph in dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"

Connected through hyperedge HE-8 (induced by source paper "core decomposition in multilayer networks theory algorithms and applications"):
  with members:
    - "on finding dense common subgraphs"
    - "mining maximal span cores from temporal networks"
    - "to be connected or not to be connected that is the minimum inefficiency subgraph problem"
    - "diversified coherent core search on multi layer graphs"
    - "best friends forever bff finding lasting dense subgraphs"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "a fast order based approach for core maintenance"
    - "i o efficient core graph decomposition at web scale"

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)