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: "a unifying optimal control framework for online job scheduling with general cost functions"
  Abstract: We consider the problem of online job scheduling on a single or multiple unrelated machines with general heterogeneous cost functions. In this model, each job $j$ has a length $v_{ij}$ and arrives with a nonnegative nondecreasing cost function $g_{ij}(t)$, if it is dispatched to machine $i$, and this information is revealed to the system upon its arrival at time $r_j$. The goal is to dispatch the jobs to the machines and process them preemptively on the machines so as to minimize the generalized completion time $\sum_{j}g_{i(j)j}(C_j)$. Here $i(j)$ refers to the machine that job $j$ is dispatched to it and $C_j$ is the completion time of job $j$. It is assumed that jobs cannot migrate between machines and each machine has a unit processing speed which can work on a single job at any time instance. In particular, we are interested in finding an online scheduling policy whose objective cost is competitive with respect to a slower optimal offline benchmark, i.e., the one which knows all the job specifications a priori. We first show that for the case of single machine and special cost functions $g_j(t)=w_jg(t)$, with nonnegative nondecreasing $g(t)$, the highest density first rule is optimal for the fractional generalized completion time. We then extend this result in the case of a single machine by giving a speed-augmented competitive algorithm for the general nondecreasing cost functions $g_j(t)$ using a novel optimal control formulation and network flow. Our approach provides a unifying and principled method of determining dual variables in various settings of online job scheduling which were done previously in an ad hoc manner or without giving much insight. Building upon this we also provide a speed-augmented competitive algorithm for multiple unrelated machines with nondecreasing convex functions $g_{ij}(t)$.

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.SY(Systems and Control)