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: "finite time and asymptotic convergence of distributed averaging and maximizing algorithms"
  Abstract: In this paper, we formulate and investigate a generalized consensus algorithm which makes an attempt to unify distributed averaging and maximizing algorithms considered in the literature. Each node iteratively updates its state as a time-varying weighted average of its own state, the minimal state, and the maximal state of its neighbors. We prove that finite-time consensus is almost impossible for averaging under this uniform model. Both time-dependent and state-dependent graphs are considered, and various necessary and/or sufficient conditions are presented on the consensus convergence. For time-dependent graphs, we show that quasi-strong connectivity is critical for averaging, as is strong connectivity for maximizing. For state-dependent graphs defined by a $\mu$-nearest-neighbor rule, where each node interacts with its $\mu$ nearest smaller neighbors and the $\mu$ nearest larger neighbors, we show that $\mu+1$ is a critical threshold on the total number of nodes for the transit from finite-time to asymptotic convergence for averaging, in the absence of node self-confidence. The threshold is $2\mu$ if each node chooses to connect only to neighbors with unique values. Numerical examples illustrate the tightness of the conditions. The results characterize some fundamental similarities and differences between distributed averaging and maximizing algorithms.

Connected through hyperedge HE-1 (induced by source paper "max consensus in sensor networks non linear bounded transmission and additive noise"):
  with members:
    - "non linear distributed average consensus using bounded transmissions"
    - "robust analog function computation via wireless multiple access channels"
    - "minimizing interference in ad hoc networks with bounded communication radius"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-2 (induced by source paper "newton like method with diagonal correction for distributed optimization"):
  with members:
    - "distributed gradient methods with variable number of working nodes"
    - "hybrid random deterministic parallel algorithms for nonconvex big data optimization"
    - "hybrid deterministic stochastic methods for data fitting"
    - "a fast distributed proximal gradient method"
    - "fast distributed gradient methods"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-3 (induced by source paper "finite time consensus using stochastic matrices with positive diagonals"):
  with members:
    - "finite time convergent gossiping"

Connected through hyperedge HE-4 (induced by source paper "fast distributed gradient methods"):
  with members:
    - "a fast distributed proximal gradient method"
    - "cooperative convex optimization in networked systems augmented lagrangian algorithms with directed gossip communication"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

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)