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: "fast emergency paths schema to overcome transient link failures in ospf routing"
  Abstract: A reliable network infrastructure must be able to sustain traffic flows, even when a failure occurs and changes the network topology. During the occurrence of a failure, routing protocols, like OSPF, take from hundreds of milliseconds to various seconds in order to converge. During this convergence period, packets might traverse a longer path or even a loop. An even worse transient behaviour is that packets are dropped even though destinations are reachable. In this context, this paper describes a proactive fast rerouting approach, named Fast Emergency Paths Schema (FEP-S), to overcome problems originating from transient link failures in OSPF routing. Extensive experiments were done using several network topologies with different dimensionality degrees. Results show that the recovery paths, obtained by FEPS, are shorter than those from other rerouting approaches and can improve the network reliability by reducing the packet loss rate during the routing protocols convergence caused by a failure.

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"
    - "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"

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"
    - "understanding the modeling of computer network delays using neural networks"
    - "knowledge defined networking"
    - "self normalizing neural networks"
    - "neural message passing for quantum chemistry"

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"
    - "understanding the modeling of computer network delays using neural networks"
    - "knowledge defined networking"
    - "self normalizing neural networks"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-4 (induced by source paper "neuroute predictive dynamic routing for software defined networks"):
  with members:
    - "a long short term memory recurrent neural network framework for network traffic matrix prediction"
    - "adam a method for stochastic optimization"
    - "faster approximation schemes for fractional multicommodity flow problems via dynamic graph algorithms"

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)