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: "distributed symmetry breaking algorithms for congested cliques"
  Abstract: The {Congested Clique} is a distributed-computing model for single-hop networks with restricted bandwidth that has been very intensively studied recently. It models a network by an $n$-vertex graph in which any pair of vertices can communicate one with another by transmitting $O(\log n )$ bits in each round. Various problems have been studied in this setting, but for some of them the best-known results are those for general networks. In this paper we devise significantly improved algorithms for various symmetry-breaking problems, such as forests-decompositions, vertex-colorings, and maximal independent set. #R##N#We analyze the running time of our algorithms as a function of the arboricity $a$ of a clique subgraph that is given as input. Our algorithms are especially efficient in Trees, planar graphs, graphs with constant genus, and many other graphs that have bounded arboricity, but unbounded size. We obtain $O(a)$-forest-decomposition algorithm with $O(\log a)$ time that improves the previously-known $O(\log n)$ time, $O(a^{2 + \epsilon})$-coloring in $O(\log^* n)$ time that improves upon an $O(\log n)$-time algorithm, $O(a)$-coloring in $O(a^{\epsilon})$-time that improves upon several previous algorithms, and a maximal independent set algorithm with $O(\sqrt a)$ time that improves at least quadratically upon the state-of-the-art for small and moderate values of $a$. #R##N#Those results are achieved using several techniques. First, we produce a forest decomposition with a helpful structure called {$H$-partition} within $O(\log a)$ rounds. In general graphs this structure requires $\Theta(\log n)$ time, but in Congested Cliques we are able to compute it faster. We employ this structure in conjunction with partitioning techniques that allow us to solve various symmetry-breaking problems efficiently.

Connected through hyperedge HE-1 (induced by source paper "distributed mst a smoothed analysis"):
  with members:
    - "fast approximate shortest paths in the congested clique"
    - "fast and efficient distributed computation of hamiltonian cycles in random graphs"
    - "congested clique algorithms for graph spanners"
    - "new distributed algorithms in almost mixing time via transformations from parallel algorithms"
    - "a simple deterministic distributed mst algorithm with near optimal time and message complexities"
    - "towards a complexity theory for the congested clique"
    - "a time and message optimal distributed algorithm for minimum spanning trees"
    - "smoothed analysis of dynamic networks"

Connected through hyperedge HE-2 (induced by source paper "simple graph coloring algorithms for congested clique and massively parallel computation"):
  with members:
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "improved massively parallel computation algorithms for mis matching and vertex cover"
    - "breaking the linear memory barrier in mpc fast mis on trees with n e memory per machine"
    - "sparse matrix multiplication with bandwidth restricted all to all communication"
    - "greedy and local ratio algorithms in the mapreduce model"
    - "mst in o 1 rounds of the congested clique"
    - "simple round compression for parallel vertex cover"

Connected through hyperedge HE-3 (induced by source paper "distributed computation in node capacitated networks"):
  with members:
    - "competitive routing in hybrid communication networks"
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "derandomizing local distributed algorithms under bandwidth restrictions"
    - "algebraic methods in the congested clique"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "construction and impromptu repair of an mst in a distributed network with o m communication"
    - "distributed approximation algorithms for weighted shortest paths"

Connected through hyperedge HE-4 (induced by source paper "congested clique algorithms for graph spanners"):
  with members:
    - "brief announcement semi mapreduce meets congested clique"
    - "improved deterministic distributed construction of spanners"
    - "transitive closure spanners"

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.DC(Distributed, Parallel, and Cluster Computing)