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: "unveiling the potential of graph neural networks for network modeling and optimization in sdn"
  Abstract: Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques do not meet the requirements to provide accurate estimations of relevant performance metrics such as delay and jitter. In this paper we propose a novel Graph Neural Network (GNN) model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter. GNN are tailored to learn and model information structured as graphs and as a result, our model is able to generalize over arbitrary topologies, routing schemes and variable traffic intensity. In the paper we show that our model provides accurate estimates of delay and jitter (worst case R2 = 0.86) when testing against topologies, routing and traffic not seen during training. In addition, we present the potential of the model for network operation by presenting several use-cases that show its effective use in per-source/destination pair delay/jitter routing optimization and its generalization capabilities by reasoning in topologies and routing schemes not seen during training.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning meets graph neural networks exploring a routing optimization use case"):
  with members:
    - "experience driven networking a deep reinforcement learning based approach"
    - "understanding the modeling of computer network delays using neural networks"
    - "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 "explaining deep learning based networked systems"):
  with members:
    - "neural packet classification"
    - "hyper sagnn a self attention based graph neural network for hypergraphs"
    - "tensorflow js machine learning for the web and beyond"
    - "pforest in network inference with random forests"
    - "interpreting and improving natural language processing in machines with natural language processing in the brain"
    - "fooling neural network interpretations via adversarial model manipulation"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "mmalfm explainable recommendation by leveraging reviews and images"

Connected through hyperedge HE-3 (induced by source paper "routenet leveraging 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-4 (induced by source paper "deep reinforcement learning meets graph neural networks an optical network routing use case"):
  with members:
    - "understanding the modeling of computer network delays using neural networks"
    - "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)