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: "tight analysis for the 3 majority consensus dynamics"
  Abstract: We present a tight analysis for the well-studied randomized 3-majority dynamics of stabilizing consensus, hence answering the main open question of Becchetti et al. [SODA'16]. #R##N#Consider a distributed system of n nodes, each initially holding an opinion in {1, 2, ..., k}. The system should converge to a setting where all (non-corrupted) nodes hold the same opinion. This consensus opinion should be \emph{valid}, meaning that it should be among the initially supported opinions, and the (fast) convergence should happen even in the presence of a malicious adversary who can corrupt a bounded number of nodes per round and in particular modify their opinions. A well-studied distributed algorithm for this problem is the 3-majority dynamics, which works as follows: per round, each node gathers three opinions --- say by taking its own and two of other nodes sampled at random --- and then it sets its opinion equal to the majority of this set; ties are broken arbitrarily, e.g., towards the node's own opinion. #R##N#Becchetti et al. [SODA'16] showed that the 3-majority dynamics converges to consensus in O((k^2\sqrt{\log n} + k\log n)(k+\log n)) rounds, even in the presence of a limited adversary. We prove that, even with a stronger adversary, the convergence happens within O(k\log n) rounds. This bound is known to be optimal.

Connected through hyperedge HE-1 (induced by source paper "on the necessary memory to compute the plurality in multi agent systems"):
  with members:
    - "distributed voting ranking with optimal number of states per node"
    - "time and space optimal counting in population protocols"
    - "stabilizing consensus with many opinions"
    - "plurality consensus in the gossip model"
    - "determining majority in networks with local interactions and very small local memory"
    - "timing in chemical reaction networks"
    - "the computational power of population protocols"

Connected through hyperedge HE-2 (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"
    - "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"
    - "plurality consensus in the gossip model"

Connected through hyperedge HE-3 (induced by source paper "phase transition of the 2 choices dynamics on core periphery networks"):
  with members:
    - "the coalescing branching random walk on expanders and the dual epidemic process"
    - "bounds on the voter model in dynamic networks"
    - "fast plurality consensus in regular expanders"
    - "discordant voting processes on finite graphs"
    - "stabilizing consensus with many opinions"
    - "core periphery in networks an axiomatic approach"
    - "the power of two choices in distributed voting"
    - "plurality consensus in the gossip model"

Connected through hyperedge HE-4 (induced by source paper "on the parallel undecided state dynamics with two colors"):
  with members:
    - "ignore or comply on breaking symmetry in consensus"
    - "bounds on the voter model in dynamic networks"
    - "stabilizing consensus with many opinions"
    - "the power of two choices in distributed voting"
    - "plurality consensus in the gossip model"
    - "determining majority in networks with local interactions and very small local memory"
    - "simple dynamics for plurality consensus"
    - "timing in chemical reaction networks"

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)