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: "heavy hitter detection entirely in the data plane"
  Abstract: Identifying the "heavy hitter" flows or flows with large traffic volumes in the data plane is important for several applications e.g., flow-size aware routing, DoS detection, and traffic engineering. However, measurement in the data plane is constrained by the need for line-rate processing (at 10-100Gb/s) and limited memory in switching hardware. We propose HashPipe, a heavy hitter detection algorithm using emerging programmable data planes. HashPipe implements a pipeline of hash tables which retain counters for heavy flows while evicting lighter flows over time. We prototype HashPipe in P4 and evaluate it with packet traces from an ISP backbone link and a data center. On the ISP trace (which contains over 400,000 flows), we find that HashPipe identifies 95% of the 300 heaviest flows with less than 80KB of memory.

Connected through hyperedge HE-1 (induced by source paper "constellation a high performance geo distributed middlebox framework"):
  with members:
    - "correctness and performance for stateful chained network functions"
    - "moongen a scriptable high speed packet generator"

Connected through hyperedge HE-2 (induced by source paper "a fast and compact invertible sketch for network wide heavy flow detection"):
  with members:
    - "optimal elephant flow detection"
    - "randomized admission policy for efficient top k and frequency estimation"
    - "amon an open source architecture for online monitoring statistical analysis and forensics of multi gigabit streams"
    - "invertible bloom lookup tables"
    - "straggler identification in round trip data streams via newton s identities and invertible bloom filters"

Connected through hyperedge HE-3 (induced by source paper "memory efficient performance monitoring on programmable switches with lean algorithms"):
  with members:
    - "optimizing bloom filter challenges solutions and comparisons"
    - "constant time updates in hierarchical heavy hitters"
    - "dapper data plane performance diagnosis of tcp"
    - "hierarchical heavy hitters with the space saving algorithm"

Connected through hyperedge HE-4 (induced by source paper "lightfdg an integrated approach to flow detection and grooming in optical wireless dcns"):
  with members:
    - "design and provision of traffic grooming for optical wireless data center networks"
    - "wireless data center networks advances challenges and opportunities"
    - "on the capacity of free space optical intensity channels"

Connected through hyperedge HE-5 (induced by source paper "d2r dataplane only policy compliant routing under failures"):
  with members:
    - "network wide configuration synthesis"
    - "efficient synthesis of network updates"
    - "merlin a language for provisioning network resources"

Connected through hyperedge HE-6 (induced by source paper "locality sensitive sketching for resilient network flow monitoring"):
  with members:
    - "constant time updates in hierarchical heavy hitters"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "abc a simple explicit congestion control protocol for wireless networks"):
  with members:
    - "congestion control and traffic management in atm networks recent advances and a survey"

Connected through hyperedge HE-8 (induced by source paper "elastic_hh tailored elastic for finding heavy hitters"):
  with members:
    - "constant time updates in hierarchical heavy hitters"

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)