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: "fault tolerant multi agent optimization part iii"
  Abstract: We study fault-tolerant distributed optimization of a sum of convex (cost) functions with real-valued scalar input/output in the presence of crash faults or Byzantine faults. In particular, the goal is to optimize a global cost function $\frac{1}{n}\sum_{i\in \mathcal{V}} h_i(x)$, where $\mathcal{V}=\{1, \ldots, n\}$ is the collection of agents, and $h_i(x)$ is agent $i$'s local cost function, which is initially known only to agent $i$. Since the above global cost function cannot be optimized exactly in presence of crash faults or Byzantine faults, we define two weaker versions of the problem for crash faults and Byzantine faults, respectively. #R##N#When some agents may crash, the goal for the weaker problem is to generate an output that is an optimum of a function formed as $$C(\sum_{i\in \mathcal{N}} h_i(x)+\sum_{i\in \mathcal{F}} \alpha_i h_i(x)),$$ where $\mathcal{N}$ is the set of non-faulty agents, $\mathcal{F}$ is the set of faulty agents (crashed agents), $0\le \alpha_i\le 1$ for each $i\in \mathcal{F}$ and $C$ is a normalization constant such that $C(|\mathcal{N}|+\sum_{i\in \mathcal{F}} \alpha_i)=1$. We present an iterative algorithm in which each agent only needs to perform local computation, and send one message per iteration. #R##N#When some agents may be Byzantine, the system cannot take full advantage of the data kept by non-faulty agents. The goal for the associated weaker problem is to generate an output that is an optimum of a function formed as $$\sum_{i\in \mathcal{N}}\alpha_i h_i(x),$$ such that $\alpha_i\geq 0$ for each $i\in \mathcal{N}$ and $\sum_{i\in \mathcal{N}}\alpha_i=1$. We present an iterative algorithm, where only local computation is needed and only one message per agent is sent in each iteration, that ensures that at least $|\mathcal{N}|-f$ agents have weights ($\alpha_i$'s) that are lower bounded by $\frac{1}{2(|\mathcal{N}|-f)}$.

Connected through hyperedge HE-1 (induced by source paper "distributed optimization for client server architecture with negative gradient weights"):
  with members:
    - "private learning on networks"
    - "distributed optimization of convex sum of non convex functions"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "fault tolerant distributed optimization part iv constrained optimization with arbitrary directed networks"):
  with members:
    - "byzantine multi agent optimization part ii"
    - "approximate consensus in highly dynamic networks the role of averaging algorithms"
    - "matrix representation of iterative approximate byzantine consensus in directed graphs"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

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)