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: "stabilizing consensus with many opinions"
  Abstract: We consider the following distributed consensus problem: Each node in a complete communication network of size $n$ initially holds an \emph{opinion}, which is chosen arbitrarily from a finite set $\Sigma$. The system must converge toward a consensus state in which all, or almost all nodes, hold the same opinion. Moreover, this opinion should be \emph{valid}, i.e., it should be one among those initially present in the system. This condition should be met even in the presence of an adaptive, malicious adversary who can modify the opinions of a bounded number of nodes in every round. #R##N#We consider the \emph{3-majority dynamics}: At every round, every node pulls the opinion from three random neighbors and sets his new opinion to the majority one (ties are broken arbitrarily). Let $k$ be the number of valid opinions. We show that, if $k \leqslant n^{\alpha}$, where $\alpha$ is a suitable positive constant, the 3-majority dynamics converges in time polynomial in $k$ and $\log n$ with high probability even in the presence of an adversary who can affect up to $o(\sqrt{n})$ nodes at each round. #R##N#Previously, the convergence of the 3-majority protocol was known for $|\Sigma| = 2$ only, with an argument that is robust to adversarial errors. On the other hand, no anonymous, uniform-gossip protocol that is robust to adversarial errors was known for $|\Sigma| > 2$.

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"
    - "tight analysis for the 3 majority consensus dynamics"
    - "time and space optimal counting in population protocols"
    - "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 "fpc bi fast probabilistic consensus within byzantine infrastructures"):
  with members:
    - "communication cost of consensus for nodes with limited memory"
    - "scalable byzantine consensus via hardware assisted secret sharing"
    - "rapid asynchronous plurality consensus"
    - "the power of two choices in distributed voting"

Connected through hyperedge HE-3 (induced by source paper "on fast probabilistic consensus in the byzantine setting"):
  with members:
    - "communication cost of consensus for nodes with limited memory"
    - "scalable byzantine consensus via hardware assisted secret sharing"
    - "rapid asynchronous plurality consensus"
    - "the power of two choices in distributed voting"

Connected through hyperedge HE-4 (induced by source paper "asynchronous majority dynamics in preferential attachment trees"):
  with members:
    - "reaching consensus via non bayesian asynchronous learning in social networks"
    - "learning in hierarchical social networks"
    - "subexponential convergence for information aggregation on regular trees"

Connected through hyperedge HE-5 (induced by source paper "minmax algorithms for stabilizing consensus"):
  with members:
    - "approximate consensus in highly dynamic networks the role of averaging algorithms"
    - "agreement in directed dynamic networks"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-6 (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"
    - "limits for rumor spreading in stochastic populations"
    - "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"

Connected through hyperedge HE-7 (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"
    - "noisy rumor spreading and plurality consensus"
    - "the power of two choices in distributed voting"
    - "plurality consensus in the gossip model"

Connected through hyperedge HE-8 (induced by source paper "phase transition of the 2 choices dynamics on core periphery networks"):
  with members:
    - "tight analysis for the 3 majority consensus dynamics"
    - "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"
    - "core periphery in networks an axiomatic approach"
    - "the power of two choices in distributed voting"
    - "plurality consensus in the gossip model"

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)