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: "simple fast and deterministic gossip and rumor spreading"
  Abstract: We study gossip algorithms for the rumor spreading problem which asks each node to deliver a rumor to all nodes in an unknown network. Gossip algorithms allow nodes only to call one neighbor per round and have recently attracted attention as message efficient, simple and robust solutions to the rumor spreading problem. #R##N#Recently, non-uniform random gossip schemes were devised to allow efficient rumor spreading in networks with bottlenecks. In particular, [Censor-Hillel et al., STOC'12] gave an O(log^3 n) algorithm to solve the 1-local broadcast problem in which each node wants to exchange rumors locally with its 1-neighborhood. By repeatedly applying this protocol one can solve the global rumor spreading quickly for all networks with small diameter, independently of the conductance. #R##N#This and all prior gossip algorithms for the rumor spreading problem have been inherently randomized in their design and analysis. This resulted in a parallel research direction trying to reduce and determine the amount of randomness needed for efficient rumor spreading. This has been done via lower bounds for restricted models and by designing gossip algorithms with a reduced need for randomness. The general intuition and consensus of these results has been that randomization plays a important role in effectively spreading rumors. #R##N#In this paper we improves over this state of the art in several ways by presenting a deterministic gossip algorithm that solves the the k-local broadcast problem in 2(k+log n)log n rounds. Besides being the first efficient deterministic solution to the rumor spreading problem this algorithm is interesting in many aspects: It is simpler, more natural, more robust and faster than its randomized pendant and guarantees success with certainty instead of with high probability. Its analysis is furthermore simple, self-contained and fundamentally different from prior works.

Connected through hyperedge HE-1 (induced by source paper "network coding gaps for completion times of multiple unicasts"):
  with members:
    - "flow cut gaps and face covers in planar graphs"
    - "new distributed algorithms in almost mixing time via transformations from parallel algorithms"
    - "faster distributed shortest path approximations via shortcuts"
    - "lower bounds for external memory integer sorting via network coding"
    - "low congestion shortcuts without embedding"
    - "cops robbers and threatening skeletons padded decomposition for minor free graphs"
    - "lexicographic products and the power of non linear network coding"
    - "distributed verification and hardness of distributed approximation"

Connected through hyperedge HE-2 (induced by source paper "message reduction in the local model is a free lunch"):
  with members:
    - "time message trade offs in distributed algorithms"
    - "broadcast and minimum spanning tree with o m messages in the asynchronous congest model"
    - "distributed spanner approximation"
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "on the complexity of local distributed graph problems"
    - "a fast network decomposition algorithm and its applications to constant time distributed computation"
    - "construction and impromptu repair of an mst in a distributed network with o m communication"
    - "improved parallel algorithms for spanners and hopsets"

Connected through hyperedge HE-3 (induced by source paper "strengthening gossip protocols using protocol dependent knowledge"):
  with members:
    - "dynamic gossip"
    - "a logic of blockchain updates"
    - "common knowledge in a logic of gossips"
    - "epistemic protocols for distributed gossiping"
    - "plurality voting under uncertainty"
    - "discovery through gossip"
    - "dominating manipulations in voting with partial information"

Connected through hyperedge HE-4 (induced by source paper "consensus needs broadcast in noiseless models but can be exponentially easier in the presence of noise"):
  with members:
    - "ignore or comply on breaking symmetry in consensus"
    - "tight analysis for the 3 majority consensus dynamics"
    - "a tight analysis of the parallel undecided state dynamics with two colors"
    - "distributed computing with channel noise"
    - "breaking the log n barrier on rumor spreading"
    - "stabilizing consensus with many opinions"
    - "noisy rumor spreading and plurality consensus"
    - "the power of two choices in distributed voting"

Connected through hyperedge HE-5 (induced by source paper "finding a bounded degree expander inside a dense one"):
  with members:
    - "expanders via local edge flips"
    - "simple parallel and distributed algorithms for spectral graph sparsification"
    - "global computation in a poorly connected world fast rumor spreading with no dependence on conductance"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-6 (induced by source paper "dynamic gossip"):
  with members:
    - "discovery through gossip"
    - "quasirandom rumor spreading"

Connected through hyperedge HE-7 (induced by source paper "slow links fast links and the cost of gossip"):
  with members:
    - "rumor spreading with bounded in degree"
    - "optimal gossip with direct addressing"
    - "radio network lower bounds made easy"
    - "global computation in a poorly connected world fast rumor spreading with no dependence on conductance"
    - "the impact of mobility on gossip algorithms"

Connected through hyperedge HE-8 (induced by source paper "gossiping with latencies"):
  with members:
    - "rumor spreading with bounded in degree"
    - "optimal gossip with direct addressing"
    - "radio network lower bounds made easy"
    - "global computation in a poorly connected world fast rumor spreading with no dependence on conductance"
    - "the impact of mobility on gossip algorithms"

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)