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: "knowledge defined networking"
  Abstract: The research community has considered in the past the application of Artificial Intelligence (AI) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control. We describe a new paradigm that accommodates and exploits SDN, NA and AI, and provide use-cases that illustrate its applicability and benefits. We also present simple experimental results that support, for some relevant use-cases, its feasibility. We refer to this new paradigm as Knowledge-Defined Networking (KDN).

Connected through hyperedge HE-1 (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"
    - "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 "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"
    - "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 "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"
    - "why and how networks should run themselves"
    - "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-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"
    - "understanding the modeling of computer network delays using neural networks"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "message passing neural networks learn little s law"):
  with members:
    - "self normalizing neural networks"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "deepplace learning to place applications in multi tenant clusters"):
  with members:
    - "device placement optimization with reinforcement learning"
    - "playing fps games with deep reinforcement learning"
    - "playing atari with deep reinforcement learning"
    - "reinforcement learning a survey"

Connected through hyperedge HE-7 (induced by source paper "artificial intelligence as a services ai aas on software defined infrastructure"):
  with members:
    - "software defined networking a comprehensive survey"

Connected through hyperedge HE-8 (induced by source paper "survey on multi access edge computing for internet of things realization"):
  with members:
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "hyperprofile based computation offloading for mobile edge networks"
    - "a survey on mobile edge computing the communication perspective"
    - "emm energy aware mobility management for mobile edge computing in ultra dense networks"
    - "realizing icn in 3gpp s 5g nextgen core architecture"
    - "virtual reality over wireless networks quality of service model and learning based resource management"

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)