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: "limits for rumor spreading in stochastic populations"
  Abstract: Biological systems can share and collectively process information to yield emergent effects, despite inherent noise in communication. While man-made systems often employ intricate structural solutions to overcome noise, the structure of many biological systems is more amorphous. It is not well understood how communication noise may affect the computational repertoire of such groups. To approach this question we consider the basic collective task of rumor spreading, in which information from few knowledgeable sources must reliably flow into the rest of the population. #R##N#In order to study the effect of communication noise on the ability of groups that lack stable structures to efficiently solve this task, we consider a noisy version of the uniform PULL model. We prove a lower bound which implies that, in the presence of even moderate levels of noise that affect all facets of the communication, no scheme can significantly outperform the trivial one in which agents have to wait until directly interacting with the sources. Our results thus show an exponential separation between the uniform PUSH and PULL communication models in the presence of noise. Such separation may be interpreted as suggesting that, in order to achieve efficient rumor spreading, a system must exhibit either some degree of structural stability or, alternatively, some facet of the communication which is immune to noise. #R##N#We corroborate our theoretical findings with a new analysis of experimental data regarding recruitment in Cataglyphis niger desert ants.

Connected through hyperedge HE-1 (induced by source paper "noidy conmunixatipn on the convergence of the averaging population protocol"):
  with members:
    - "almost logarithmic time space optimal leader election in population protocols"
    - "fast space optimal leader election in population protocols"
    - "erasure correction for noisy radio networks"
    - "ignore or comply on breaking symmetry in consensus"
    - "broadcasting in noisy radio networks"
    - "space optimal majority in population protocols"
    - "rapid asynchronous plurality consensus"
    - "efficient k party voting with two choices"

Connected through hyperedge HE-2 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics"):
  with members:
    - "pooling or sampling collective dynamics for electrical flow estimation"
    - "phase transition of the 2 choices dynamics on core periphery networks"
    - "the ants problem"
    - "ignore or comply on breaking symmetry in consensus"
    - "distributed graph clustering and sparsification"
    - "average whenever you meet opportunistic protocols for community detection"
    - "a tight analysis of the parallel undecided state dynamics with two colors"
    - "on the parallel undecided state dynamics with two colors"

Connected through hyperedge HE-3 (induced by source paper "ant inspired density estimation via random walks"):
  with members:
    - "on coalescence time in graphs when is coalescing as fast as meeting"
    - "graph size estimation"
    - "a walk in facebook uniform sampling of users in online social networks"

Connected through hyperedge HE-4 (induced by source paper "noisy rumor spreading and plurality consensus"):
  with members:
    - "global majority consensus by local majority polling on graphs of a given degree sequence"
    - "space optimal majority in population protocols"
    - "minimizing message size in stochastic communication patterns fast self stabilizing protocols with 3 bits"
    - "noisy information spreading and plurality consensus"
    - "the power of two choices in distributed voting"
    - "plurality consensus in the gossip model"
    - "breathe before speaking efficient information dissemination despite noisy limited and anonymous communication"
    - "determining majority in networks with local interactions and very small local memory"

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.MA(Multiagent Systems)