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: "unsupervised machine learning for networking techniques applications and research challenges"
  Abstract: While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.

Connected through hyperedge HE-1 (induced by source paper "thirty years of machine learning the road to pareto optimal next generation wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "budget constrained multi armed bandits with multiple plays"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
    - "recent trends in deep learning based natural language processing"
    - "detection algorithms for communication systems using deep learning"

Connected through hyperedge HE-2 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection"):
  with members:
    - "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000"
    - "transferable knowledge for low cost decision making in cloud environments"
    - "rectified decision trees towards interpretability compression and empirical soundness"
    - "the university of cambridge s machine translation systems for wmt18"
    - "multi representation ensembles and delayed sgd updates improve syntax based nmt"
    - "an analysis of deep neural network models for practical applications"
    - "fake it till you make it fishing for catfishes"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-3 (induced by source paper "leveraging machine learning and big data for smart buildings a comprehensive survey"):
  with members:
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "machine learning for internet of things data analysis a survey"
    - "microlocation for internet of things equipped smart buildings"
    - "deep activity recognition models with triaxial accelerometers"
    - "context aware computing for the internet of things a survey"
    - "regression with linear factored functions"

Connected through hyperedge HE-4 (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"
    - "machine learning for networking workflow advances and opportunities"
    - "deep learning in mobile and wireless networking a survey"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "wireless communications in the era of big data"

Connected through hyperedge HE-5 (induced by source paper "the softwarised network data zoo"):
  with members:
    - "jasper joint optimization of scaling placement and routing of virtual network services"
    - "machine learning for networking workflow advances and opportunities"
    - "understand your chains towards performance profile based network service management"
    - "medicine rapid prototyping of production ready network services in multi pop environments"

Connected through hyperedge HE-6 (induced by source paper "transferable knowledge for low cost decision making in cloud environments"):
  with members:
    - "cloud brokerage a systematic survey"
    - "daleel simplifying cloud instance selection using machine learning"
    - "patterns in the chaos a study of performance variation and predictability in public iaas clouds"
    - "cloud benchmarking for performance"

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"
    - "why and how networks should run themselves"

Connected through hyperedge HE-8 (induced by source paper "enabling trust in deep learning models a digital forensics case study"):
  with members:
    - "cloudme forensics a case of big data forensic investigation"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "autoencoder with recurrent neural networks for video forgery detection"
    - "attacking automatic video analysis algorithms a case study of google cloud video intelligence api"
    - "axiomatic attribution for deep networks"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"

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)