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: "machine learning for networking workflow advances and opportunities"
  Abstract: Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the networking and distributed computing system is the key infrastructure to provide efficient computational resources for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of MLN, which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations of their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities in networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help motivate researchers to develop innovative algorithms, standards and frameworks.

Connected through hyperedge HE-1 (induced by source paper "machine learning for intelligent optical networks a comprehensive survey"):
  with members:
    - "adaptive federated learning in resource constrained edge computing systems"
    - "artificial intelligence ai methods in optical networks a comprehensive survey"
    - "deep learning for iot big data and streaming analytics a survey"
    - "deep reinforcement learning a brief survey"
    - "federated learning of deep networks using model averaging"
    - "asynchronous methods for deep reinforcement learning"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "a survey on recent advances in transport layer protocols"):
  with members:
    - "a quic implementation for ns 3"
    - "beyond socket options making the linux tcp stack truly extensible"
    - "low delay scheduling of objects over multiple wireless paths"
    - "elasticity detection a building block for delay sensitive congestion control"
    - "datacenter traffic control understanding techniques and tradeoffs"
    - "qaware a cross layer approach to mptcp scheduling"
    - "homa a receiver driven low latency transport protocol using network priorities"
    - "mptcp meets fec supporting latency sensitive applications over heterogeneous networks"

Connected through hyperedge HE-3 (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"
    - "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 "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"
    - "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-5 (induced by source paper "towards 6g networks use cases and technologies"):
  with members:
    - "6g the next frontier"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "towards 5g enabled tactile robotic telesurgery"
    - "millimeter wave beam selection using out of band spatial information"
    - "millimeter wave vehicular communication to support massive automotive sensing"

Connected through hyperedge HE-6 (induced by source paper "a flexible machine learning aware architecture for future wlans"):
  with members:
    - "komondor a wireless network simulator for next generation high density wlans"
    - "deep learning in mobile and wireless networking a survey"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "wireless communications in the era of big data"

Connected through hyperedge HE-7 (induced by source paper "the softwarised network data zoo"):
  with members:
    - "jasper joint optimization of scaling placement and routing of virtual network services"
    - "understand your chains towards performance profile based network service management"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "medicine rapid prototyping of production ready network services in multi pop environments"

Connected through hyperedge HE-8 (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"
    - "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"
    - "why and how networks should run themselves"
    - "unsupervised machine learning for networking techniques applications and research challenges"

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)