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: "mst in o 1 rounds of the congested clique"
  Abstract: We present a distributed randomized algorithm finding Minimum Spanning Tree (MST) of a given graph in O(1) rounds, with high probability, in the Congested Clique model. The input graph in the Congested Clique model is a graph of n nodes, where each node initially knows only its incident edges. The communication graph is a clique with limited edge bandwidth: each two nodes (not necessarily neighbours in the input graph) can exchange $O(\log n)$ bits. #R##N#As in previous works, the key part of the MST algorithm is an efficient Connected Components (CC) algorithm. However, unlike the former approaches, we do not aim at simulating the standard Boruvka algorithm, at least at initial stages of the CC algorithm. Instead, we develop a new technique which combines connected components of sample sparse subgraphs of the input graph in order to accelerate the process of uncovering connected components of the original input graph. More specifically, we develop a sparsification technique which reduces an initial CC problem in $O(1)$ rounds to its two restricted instances. The former instance has a graph with maximal degree $O(\log \log n)$ as the input -- here our sample-combining technique helps. In the latter instance, a partition of the input graph into $O(n/\log \log n)$ connected components is known. This gives an opportunity to apply previous algorithms to determine connected components in $O(1)$ rounds. #R##N#Our result addresses the problem from and the $O(\log \log n)$ algorithm of Lotker et al. [SPAA 2003; SICOMP 2005], improves over previous $O(\log* n)$ algorithm of Ghaffari et al. [PODC 2016] and $O(\log \log \log n)$ algorithm of Hegeman et al. [PODC 2015] . It also determines $\Theta(1)$ round complexity in the congested clique for MST, as well as other graph problems, including bipartiteness, cut verification, s-t connectivity and cycle containment.

Connected through hyperedge HE-1 (induced by source paper "how fast can you update your mst dynamic algorithms for cluster computing"):
  with members:
    - "parallel batch dynamic graph connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "almost optimal distributed algorithms for large scale graph problems"
    - "optimal deterministic routing and sorting on the congested clique"
    - "sorting searching and simulation in the mapreduce framework"

Connected through hyperedge HE-2 (induced by source paper "fast approximate shortest paths in the congested clique"):
  with members:
    - "delta 1 coloring in the congested clique model"
    - "congested clique algorithms for graph spanners"
    - "sparse matrix multiplication and triangle listing in the congested clique model"
    - "towards tight approximation bounds for graph diameter and eccentricities"
    - "a deterministic distributed algorithm for exact weighted all pairs shortest paths in tilde o n 3 2 rounds"
    - "distributed triangle detection via expander decomposition"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "linear size hopsets with small hopbound and distributed routing with low memory"

Connected through hyperedge HE-3 (induced by source paper "improved distributed expander decomposition and nearly optimal triangle enumeration"):
  with members:
    - "distributed edge connectivity in sublinear time"
    - "sparse matrix multiplication and triangle listing in the congested clique model"
    - "new distributed algorithms in almost mixing time via transformations from parallel algorithms"
    - "distributed triangle detection via expander decomposition"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "expander decomposition and pruning faster stronger and simpler"
    - "fooling views a new lower bound technique for distributed computations under congestion"
    - "triangle finding and listing in congest networks"

Connected through hyperedge HE-4 (induced by source paper "faster algorithms for edge connectivity via random 2 out contractions"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "distributed edge connectivity in sublinear time"
    - "random k out subgraph leaves only o n k inter component edges"
    - "exponentially faster massively parallel maximal matching"
    - "breaking the linear memory barrier in mpc fast mis on trees with strongly sublinear memory"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"

Connected through hyperedge HE-5 (induced by source paper "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"):
  with members:
    - "simple concurrent labeling algorithms for connected components"
    - "simple graph coloring algorithms for congested clique and massively parallel computation"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "parallel graph connectivity in log diameter rounds"
    - "brief announcement semi mapreduce meets congested clique"
    - "massively parallel symmetry breaking on sparse graphs mis and maximal matching"
    - "round compression for parallel graph algorithms in strongly sublinear space"

Connected through hyperedge HE-6 (induced by source paper "shortest paths in a hybrid network model"):
  with members:
    - "fast approximate shortest paths in the congested clique"
    - "a deterministic distributed algorithm for exact weighted all pairs shortest paths in tilde o n 3 2 rounds"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "a faster distributed single source shortest paths algorithm"
    - "distributed exact shortest paths in sublinear time"
    - "improved distributed algorithms for exact shortest paths"
    - "competitive routing in hybrid communication networks"
    - "near linear lower bounds for distributed distance computations even in sparse networks"

Connected through hyperedge HE-7 (induced by source paper "distributed approximation algorithms for steiner tree in the mathcal congested mathcal clique"):
  with members:
    - "connectivity lower bounds in broadcast congested clique"
    - "super fast mst algorithms in the congested clique using o m messages"
    - "algebraic methods in the congested clique"
    - "approximation of distances and shortest paths in the broadcast congest clique"
    - "fast partial distance estimation and applications"
    - "distributed approximation algorithms for weighted shortest paths"
    - "near constant time distributed algorithms on a congested clique"
    - "super fast distributed algorithms for metric facility location"

Connected through hyperedge HE-8 (induced by source paper "connectivity lower bounds in broadcast congested clique"):
  with members:
    - "detecting cliques in congest networks"
    - "fooling views a new lower bound technique for distributed computations under congestion"
    - "proof labeling schemes broadcast unicast and in between"
    - "quadratic and near quadratic lower bounds for the congest model"
    - "deterministic graph connectivity in the broadcast congested clique"
    - "approximation of distances and shortest paths in the broadcast congest clique"
    - "distributed verification and hardness of distributed approximation"

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)