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: "discrete load balancing in heterogeneous networks with a focus on second order diffusion"
  Abstract: In this paper we consider a wide class of discrete diffusion load balancing algorithms. The problem is defined as follows. We are given an interconnection network and a number of load items, which are arbitrarily distributed among the nodes of the network. The goal is to redistribute the load in iterative discrete steps such that at the end each node has (almost) the same number of items. In diffusion load balancing nodes are only allowed to balance their load with their direct neighbors. #R##N#We show three main results. Firstly, we present a general framework for randomly rounding the flow generated by continuous diffusion schemes over the edges of a graph in order to obtain corresponding discrete schemes. Compared to the results of Rabani, Sinclair, and Wanka, FOCS'98, which are only valid w.r.t. the class of homogeneous first order schemes, our framework can be used to analyze a larger class of diffusion algorithms, such as algorithms for heterogeneous networks and second order schemes. Secondly, we bound the deviation between randomized second order schemes and their continuous counterparts. Finally, we provide a bound for the minimum initial load in a network that is sufficient to prevent the occurrence of negative load at a node during the execution of second order diffusion schemes. #R##N#Our theoretical results are complemented with extensive simulations on different graph classes. We show empirically that second order schemes, which are usually much faster than first order schemes, will not balance the load completely on a number of networks within reasonable time. However, the maximum load difference at the end seems to be bounded by a constant value, which can be further decreased if first order scheme is applied once this value is achieved by second order scheme.

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)