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: "the environmental footprint of a distributed cloud storage"
  Abstract: Every time we access a file on the Cloud, a chain of routing servers is activated to transfer the payload from the storage facility to the user's device, and back. The data center, being densely packed and highly-performant, is hardly optimized in terms of energy consumption and environmental impact. The ICT (information communication technologies) ecosystem is estimated to be responsible for the 10 percent of the full worldwide energy consumption - equivalent to Germany and Japan total energy demand. However, being a fast-inflating and almost unregulated market, the environmental footprint caused by data storage and transfer on the internet shows no signs of slowing down. In this paper, we analyze a reversal paradigm for cloud storage (implemented by Cubbit - www.cubbit.io) in which data are stored and distributed over a network of p2p-interacting single board computers. Here we compare Cubbit to the traditional server-based solution in terms of environmental footprint and power/usage efficiency. First, being virtualized and distributed upon small single-board computers, storage is far more efficient than data centers racks and does not need any additional cooling. Secondly, the distributed architecture can leverage proximity of files to the user (e.g. within the same city) to minimize the consumption due to the powered route from the virtualized storage facility to the user's device. We quantify both these effects using the internet model published by Baliga et al. (Proceedings of the IEEE 99.1, 2011), and compare the estimation with the same model applied to a server-based cloud storage (e.g. Dropbox). Results show that a remarkable reduction in terms of energy consumption is obtained on both storage (less than 1/10 of consumptions) and transfers (c.a. 1/2 of consumptions).

Connected through hyperedge HE-1 (induced by source paper "energy usage reports environmental awareness as part of algorithmic accountability"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "green ai"
    - "the dataset nutrition label a framework to drive higher data quality standards"

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)