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: "byzantine multi agent optimization part ii"
  Abstract: We study Byzantine fault-tolerant distributed optimization of a sum of convex (cost) functions with real-valued scalar input/ouput. In particular, the goal is to optimize a global cost function $\frac{1}{|\mathcal{N}|}\sum_{i\in \mathcal{N}} h_i(x)$, where $\mathcal{N}$ is the set of non-faulty agents, and $h_i(x)$ is agent $i$'s local cost function, which is initially known only to agent $i$. In general, when some of the agents may be Byzantine faulty, the above goal is unachievable, because the identity of the faulty agents is not necessarily known to the non-faulty agents, and the faulty agents may behave arbitrarily. Since the above global cost function cannot be optimized exactly in presence of Byzantine agents, we define a weaker version of the problem. #R##N#The goal for the weaker problem is to generate an output that is an optimum of a function formed as a convex combination of local cost functions of the non-faulty agents. More precisely, for some choice of weights $\alpha_i$ for $i\in \mathcal{N}$ such that $\alpha_i\geq 0$ and $\sum_{i\in \mathcal{N}}\alpha_i=1$, the output must be an optimum of the cost function $\sum_{i\in \mathcal{N}} \alpha_ih_i(x)$. Ideally, we would like $\alpha_i=\frac{1}{|\mathcal{N}|}$ for all $i\in \mathcal{N}$ -- however, this cannot be guaranteed due to the presence of faulty agents. In fact, we show that the maximum achievable number of nonzero weights ($\alpha_i$'s) is $|\mathcal{N}|-f$, where $f$ is the upper bound on the number of Byzantine agents. In addition, we present algorithms that ensure that at least $|\mathcal{N}|-f$ agents have weights that are bounded away from 0. We also propose a low-complexity suboptimal algorithm, which ensures that at least $\lceil \frac{n}{2}\rceil-\phi$ agents have weights that are bounded away from 0, where $n$ is the total number of agents, and $\phi$ ($\phi\le f$) is the actual number of Byzantine agents.

Connected through hyperedge HE-1 (induced by source paper "secure distributed state estimation of an lti system over time varying networks and analog erasure channels"):
  with members:
    - "a distributed observer for a time invariant linear system"
    - "resilient consensus of second order agent networks asynchronous update rules with delays"
    - "detection of biasing attacks on distributed estimation networks"
    - "distributed observers for lti systems"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-2 (induced by source paper "byrdie byzantine resilient distributed coordinate descent for decentralized learning"):
  with members:
    - "byzantine stochastic gradient descent"
    - "securing distributed machine learning in high dimensions"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "defending against saddle point attack in byzantine robust distributed learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "cloud k svd a collaborative dictionary learning algorithm for big distributed data"
    - "fault tolerant distributed optimization part iv constrained optimization with arbitrary directed networks"

Connected through hyperedge HE-3 (induced by source paper "distributed optimization under adversarial nodes"):
  with members:
    - "matrix representation of iterative approximate byzantine consensus in directed graphs"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-4 (induced by source paper "fault tolerant distributed optimization part iv constrained optimization with arbitrary directed networks"):
  with members:
    - "fault tolerant multi agent optimization part iii"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "fault tolerant multi agent optimization part iii"):
  with members:
    - "data falsification attacks on consensus based detection systems"
    - "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)