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: "message passing neural networks learn little s law"
  Abstract: This letter presents a solution to the problem of universal representation of graphs exemplifying communication network topologies with the help of neural networks. The proposed approach is based on  message-passing neural networks . The approach enables us to represent topologies and operational aspects of networks. The usefulness of the solution is illustrated with a case study of delay prediction in queuing networks. This shows that performance evaluation can be provided without having to apply complex modeling. In consequence, the proposed solution makes it possible to effectively apply the methods elaborated in the field of machine learning in communications.

Connected through hyperedge HE-1 (induced by source paper "unveiling the potential of graph neural networks for network modeling and optimization in sdn"):
  with members:
    - "experience driven networking a deep reinforcement learning based approach"
    - "relational inductive biases deep learning and graph networks"
    - "machine learning for networking workflow advances and opportunities"
    - "understanding the modeling of computer network delays using neural networks"
    - "knowledge defined networking"
    - "self normalizing neural networks"
    - "neural message passing for quantum chemistry"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "on the robustness of deep learning predicted contention models for network calculus"):
  with members:
    - "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"
    - "a theory of traffic regulators for deterministic networks with application to interleaved regulators"
    - "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"

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)