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: "greening big data networks the impact of veracity"
  Abstract: The continuous increase in big data applications, in number and types, creates new challenges that should be tackled by the green ICT community. Big data is mainly characterized by 4 Vs volume, variety, velocity, and veracity. Each V poses a number of challenges that have implications on the energy efficiency of the underlying networks carrying the big data. Addressing the veracity of the data is a more serious challenge to data scientists, since they need to distinguish between the meaningful data and the dirty data. In this article, we investigate the impact of big data veracity on greening IP by developing a Mixed Integer Linear Programming, MILP, model that encapsulates the distinctive features of veracity. In our analyses, the big data network was greened by cleansing the raw big data before processing and then progressively processing the cleansed big data at strategic locations, dubbed processing nodes, PNs. The PNs are built into the network along the path from the sources to the centralized datacenters. At each PN, the cleansed data was processed and smaller volume of useful information was extracted progressively, thereby, reducing the network power consumption. Furthermore, a backup for the cleansed data was stored in an optimally selected Backup Node, BN. We evaluated the network power saving that can be achieved by a green big data network compared to the classical non-progressive approach. We obtained up to 52 percent network power savings, on average, in the green big data approach compared to the classical approach.

Connected through hyperedge HE-1 (induced by source paper "energy efficient software matching in vehicular fog"):
  with members:
    - "bounds for energy efficient survivable ip over wdm networks with network coding"

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.NI(Networking and Internet Architecture)