human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "noidy conmunixatipn on the convergence of the averaging population protocol"):
  Abstract: We study a process of \emph{averaging} in a distributed system with \emph{noisy communication}. Each of the agents in the system starts with some value and the goal of each agent is to compute the average of all the initial values. In each round, one pair of agents is drawn uniformly at random from the whole population, communicates with each other and each of these two agents updates their local value based on their own value and the received message. The communication is noisy and whenever an agent sends any value $v$, the receiving agent receives $v+N$, where $N$ is a zero-mean Gaussian random variable. The two quality measures of interest are (i) the total sum of squares $TSS(t)$, which measures the sum of square distances from the average load to the \emph{initial average} and (ii) $\bar{\phi}(t)$, measures the sum of square distances from the average load to the \emph{running average} (average at time $t$). #R##N#It is known that the simple averaging protocol---in which an agent sends its current value and sets its new value to the average of the received value and its current value---converges eventually to a state where $\bar{\phi}(t)$ is small. #R##N#It has been observed that $TSS(t)$, due to the noise, eventually diverges and previous research---mostly in control theory---has focused on showing eventual convergence w.r.t. the running average. #R##N#We obtain the first probabilistic bounds on the convergence time of $\bar{\phi}(t)$ and precise bounds on the drift of $TSS(t)$ that show that albeit $TSS(t)$ eventually diverges, for a wide and interesting range of parameters, $TSS(t)$ stays small for a number of rounds that is polynomial in the number of agents. #R##N#Our results extend to the synchronous setting and settings where the agents are restricted to discrete values and perform rounding.

Includes members:
  Member "almost logarithmic time space optimal leader election in population protocols":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the power of global knowledge on self stabilizing population protocols")
  Member "fast space optimal leader election in population protocols":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust comparison in population protocols")
  Member "erasure correction for noisy radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "noisy beeping networks")
  Member "limits for rumor spreading in stochastic populations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics")
  Member "ignore or comply on breaking symmetry in consensus":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics")
  Member "broadcasting in noisy radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "noisy beeping networks")
  Member "space optimal majority in population protocols":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust comparison in population protocols")
  Member "rapid asynchronous plurality consensus":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fpc bi fast probabilistic consensus within byzantine infrastructures")

Question: Please tell me which class the source paper 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)