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: "repetita repeatable experiments for performance evaluation of traffic engineering algorithms"
  Abstract: In this paper, we propose a pragmatic approach to improve reproducibility of experimental analyses of traffic engineering (TE) algorithms, whose implementation, evaluation and comparison are currently hard to replicate. Our envisioned goal is to enable universally-checkable experiments of existing and future TE algorithms. We describe the design and implementation of REPETITA, a software framework that implements common TE functions, automates experimental setup, and eases comparisons (in terms of solution quality, execution time, etc.) of TE algorithms. In its current version, REPETITA includes (i) a dataset for repeatable experiments, consisting of more than 250 real network topologies with complete bandwidth and delay information as well as associated traffic matrices; and (ii) the implementation of state-of-the-art algorithms for intra-domain TE with IGP weight tweaking and Segment Routing optimization. We showcase how our framework can successfully reproduce results described in the literature, and ease new analyses of qualitatively-diverse TE algorithms. We publicly release our REPETITA implementation, hoping that the community will consider it as a demonstration of feasibility, an incentive and an initial code basis for improving experiment reproducibility: Its plugin-oriented architecture indeed makes REPETITA easy to extend with new datasets, algorithms, TE primitives and analyses. We therefore invite the research community to use and contribute to our released code and dataset.

Connected through hyperedge HE-1 (induced by source paper "toward demand aware networking a theory for self adjusting networks"):
  with members:
    - "datacenter traffic control understanding techniques and tradeoffs"
    - "virtual network embedding approximations leveraging randomized rounding"
    - "demand aware network designs of bounded degree"
    - "concurrent self adjusting distributed tree networks"
    - "online balanced repartitioning"

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)