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: "understanding the modeling of computer network delays using neural networks"
  Abstract: Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. In this context, ML can be used as a computer network modeling technique to build models that estimate the network performance. Indeed, network modeling is a central technique to many networking functions, for instance in the field of optimization, in which the model is used to search a configuration that satisfies the target policy. In this paper, we aim to provide an answer to the following question: Can neural networks accurately model the delay of a computer network as a function of the input traffic? For this, we assume the network as a black-box that has as input a traffic matrix and as output delays. Then we train different neural networks models and evaluate its accuracy under different fundamental network characteristics: topology, size, traffic intensity and routing. With this, we aim to have a better understanding of computer network modeling with neural nets and ultimately provide practical guidelines on how such models need to be trained.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning meets graph neural networks exploring a routing optimization use case"):
  with members:
    - "unveiling the potential of graph neural networks for network modeling and optimization in sdn"
    - "experience driven networking a deep reinforcement learning based approach"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "interaction networks for learning about objects relations and physics"
    - "playing atari with deep reinforcement learning"
    - "fast emergency paths schema to overcome transient link failures in ospf routing"

Connected through hyperedge HE-2 (induced by source paper "routenet leveraging graph neural networks for network modeling and optimization in sdn"):
  with members:
    - "unveiling the potential of graph neural networks for network modeling and optimization in sdn"
    - "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"
    - "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-3 (induced by source paper "unveiling the potential of graph neural networks for network modeling and optimization in sdn"):
  with members:
    - "message passing neural networks learn little s law"
    - "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"
    - "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-4 (induced by source paper "deep reinforcement learning meets graph neural networks an optical network routing use case"):
  with members:
    - "unveiling the potential of graph neural networks for network modeling and optimization in sdn"
    - "knowledge defined networking"
    - "neural message passing for quantum chemistry"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "interaction networks for learning about objects relations and physics"
    - "playing atari with deep reinforcement learning"

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)