human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "taurus an intelligent data plane"):
  Abstract: Emerging applications -- cloud computing, the internet of things, and augmented/virtual reality -- need responsive, available, secure, ubiquitous, and scalable datacenter networks. Network management currently uses simple, per-packet, data-plane heuristics (e.g., ECMP and sketches) under an intelligent, millisecond-latency control plane that runs data-driven performance and security policies. However, to meet users' quality-of-service expectations in a modern data center, networks must operate intelligently at line rate. In this paper, we present Taurus, an intelligent data plane capable of machine-learning inference at line rate. Taurus adds custom hardware based on a map-reduce abstraction to programmable network devices, such as switches and NICs; this new hardware uses pipelined and SIMD parallelism for fast inference. Our evaluation of a Taurus-enabled switch ASIC -- supporting several real-world benchmarks -- shows that Taurus operates three orders of magnitude faster than a server-based control plane, while increasing area by 24% and latency, on average, by 178 ns. On the long road to self-driving networks, Taurus is the equivalent of adaptive cruise control: deterministic rules steer flows, while machine learning tunes performance and heightens security.

Includes members:
  Member "scaling distributed machine learning with in network aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on biased compression for distributed learning")
  Member "meta learning neural bloom filters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive learned bloom filter ada bf efficient utilization of the classifier")
  Member "fpga based accelerators of deep learning networks for learning and classification a review":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning fitness functions for genetic algorithms")
  Member "deep neural network approximation for custom hardware where we ve been where we re going":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recurrent neural networks an embedded computing perspective")
  Member "experience driven networking a deep reinforcement learning based approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning")
  Member "in network neural networks":
    also connected through other hyperedges:
      (none)
  Member "structured control nets for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generalization in reinforcement learning with selective noise injection and information bottleneck")
  Member "training deep neural networks with 8 bit floating point numbers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse weight activation training")

Question: Please tell me which class the source paper 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)