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 streaming and mapreduce"
  Abstract: The problem of finding locally dense components of a graph is an important primitive in data analysis, with wide-ranging applications from community mining to spam detection and the discovery of biological network modules. In this paper we present new algorithms for finding the densest subgraph in the streaming model. For any epsilon>0, our algorithms make O((log n)/log (1+epsilon)) passes over the input and find a subgraph whose density is guaranteed to be within a factor 2(1+epsilon) of the optimum. Our algorithms are also easily parallelizable and we illustrate this by realizing them in the MapReduce model. In addition we perform extensive experimental evaluation on massive real-world graphs showing the performance and scalability of our algorithms in practice.

Connected through hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations"):
  with members:
    - "walking randomly massively and efficiently"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "a conditional lower bound on graph connectivity in mapreduce"
    - "efficient circuit simulation in mapreduce"
    - "exponentially faster massively parallel maximal matching"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "massively parallel algorithms for finding well connected components in sparse graphs"

Connected through hyperedge HE-2 (induced by source paper "parallel clique counting and peeling algorithms"):
  with members:
    - "motivo fast motif counting via succinct color coding and adaptive sampling"
    - "graphlet decomposition framework algorithms and applications"
    - "why do simple algorithms for triangle enumeration work in the real world"

Connected through hyperedge HE-3 (induced by source paper "computing the k densest subgraphs of a graph"):
  with members:
    - "top k overlapping densest subgraphs approximation and complexity"
    - "density friendly graph decomposition"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-4 (induced by source paper "parallel batch dynamic graphs algorithms and lower bounds"):
  with members:
    - "parallel batch dynamic graph connectivity"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "a conditional lower bound on graph connectivity in mapreduce"
    - "exponentially faster massively parallel maximal matching"
    - "distributed and streaming linear programming in low dimensions"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"

Connected through hyperedge HE-5 (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-6 (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-7 (induced by source paper "mapreduce meets fine grained complexity mapreduce algorithms for apsp matrix multiplication 3 sum and beyond"):
  with members:
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "truly sub cubic algorithms for language edit distance and rna folding via fast bounded difference min plus product"
    - "random coordinate descent methods for minimizing decomposable submodular functions"
    - "powers of tensors and fast matrix multiplication"
    - "parallel algorithms for geometric graph problems"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "exact weight subgraphs and the k sum conjecture"
    - "communication steps for parallel query processing"

Connected through hyperedge HE-8 (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"
    - "densest subgraph in dynamic graph streams"
    - "local conflict coloring"
    - "hypergraph two coloring in the streaming model"

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.DB(Databases)