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: "a theory of traffic regulators for deterministic networks with application to interleaved regulators"
  Abstract: We define the minimal interleaved regulator, which generalizes the Urgency Based Shaper that was recently proposed by Specht and Samii as a simpler alternative to per-flow reshaping in deterministic networks with aggregate scheduling. With this regulator, packets of multiple flows are processed in one FIFO queue; the packet at the head of the queue is examined against the regulation constraints of its flow; it is released at the earliest time at which this is possible without violating the constraints. Packets that are not at the head of the queue are not examined until they reach the head of the queue. This regulator thus possibly delays the packet at the head of the queue but also all following packets, which typically belong to other flows. However, we show that, when it is placed after an arbitrary FIFO system, the worst case delay of the combination is not increased. This shaping-for-free property is well-known with per-flow shapers; surprisingly, it continues to hold here. To derive this property, we introduce a new definition of traffic regulator, the minimal Pi-regulator, which extends both the greedy shaper of network calculus and Chang's max-plus regulator and also includes new types of regulators such as packet rate limiters. Incidentally, we provide a new insight on the equivalence between min-plus and max-plus formulations of regulators and shapers.

Connected through hyperedge HE-1 (induced by source paper "on time synchronization issues in time sensitive networks with regulators and nonideal clocks"):
  with members:
    - "improved delay bound for a service curve element with known transmission rate"

Connected through hyperedge HE-2 (induced by source paper "on the robustness of deep learning predicted contention models for network calculus"):
  with members:
    - "message passing neural networks learn little s law"
    - "routenet leveraging graph neural networks for network modeling and optimization in sdn"
    - "learning a sat solver from single bit supervision"
    - "relational inductive biases deep learning and graph networks"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "quality and cost of deterministic network calculus design and evaluation of an accurate and fast analysis"
    - "convolutional networks on graphs for learning molecular fingerprints"

Connected through hyperedge HE-3 (induced by source paper "tsn algorithms for large scale networks a survey and conceptual comparison"):
  with members:
    - "ultra low latency ull networks the ieee tsn and ietf detnet standards and related 5g ull research"
    - "improved delay bound for a service curve element with known transmission rate"
    - "end to end latency and backlog bounds in time sensitive networking with credit based shapers and asynchronous traffic shaping"
    - "on the fly erasure coding for real time video applications"

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)