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: "why and how networks should run themselves"
  Abstract: The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols.

Connected through hyperedge HE-1 (induced by source paper "fog computing systems state of the art research issues and future trends"):
  with members:
    - "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges"
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a blockchain enabled trustless crowd intelligence ecosystem on mobile edge computing"
    - "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "mitigating jamming attacks using energy harvesting"
    - "game theoretical modelling of network cybersecurity"

Connected through hyperedge HE-2 (induced by source paper "adversarial machine learning attack on modulation classification"):
  with members:
    - "adversarial examples in rf deep learning detection of the attack and its physical robustness"
    - "adversarial attacks on deep learning based radio signal classification"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "knowledge defined networking"
    - "unsupervised representation learning of structured radio communication signals"
    - "convolutional radio modulation recognition networks"
    - "towards evaluating the robustness of neural networks"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "the adversarial machine learning conundrum can the insecurity of ml become the achilles heel of cognitive networks"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial attacks on cognitive self organizing networks the challenge and the way forward"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "towards evaluating the robustness of neural networks"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "the limitations of deep learning in adversarial settings"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "distributed consistent network updates in sdns local verification for global guarantees"):
  with members:
    - "survey of consistent software defined network updates"
    - "on the power of preprocessing in decentralized network optimization"
    - "timed consistent network updates"
    - "brief announcement on the resilience of routing tables"

Connected through hyperedge HE-5 (induced by source paper "black box adversarial ml attack on modulation classification"):
  with members:
    - "adversarial examples in rf deep learning detection of the attack and its physical robustness"
    - "adversarial attacks on deep learning based radio signal classification"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-6 (induced by source paper "pforest in network inference with random forests"):
  with members:
    - "distilling a neural network into a soft decision tree"

Connected through hyperedge HE-7 (induced by source paper "adversarial attacks on cognitive self organizing networks the challenge and the way forward"):
  with members:
    - "experience driven networking a deep reinforcement learning based approach"
    - "divide denoise and defend against adversarial attacks"
    - "machine learning for networking workflow advances and opportunities"
    - "deep learning in mobile and wireless networking a survey"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "knowledge defined networking"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "unsupervised machine learning for networking techniques applications and research challenges"

Connected through hyperedge HE-8 (induced by source paper "on analyzing self driving networks a systems thinking approach"):
  with members:
    - "knowledge defined networking"
    - "taming limits with approximate networking"

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)