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: "internet of things applications and challenges in technology and standardization"
  Abstract: The phrase Internet of Things (IoT) heralds a vision of the future Internet where connecting physical things, from banknotes to bicycles, through a network will let them take an active part in the Internet, exchanging information about themselves and their surroundings. This will give immediate access to information about the physical world and the objects in it leading to innovative services and increase in efficiency and productivity. This paper studies the state-of-the-art of IoT and presents the key technological drivers,potential applications, challenges and future research areas in the domain of IoT. IoT definitions from different perspective in academic and industry communities are also discussed and compared. Finally some major issues of future research in IoT are identified and discussed briefly.

Connected through hyperedge HE-1 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "machine learning in iot security current solutions and future challenges"
    - "toward intelligent vehicular networks a machine learning framework"
    - "distributed learning for channel allocation over a shared spectrum"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-2 (induced by source paper "machine learning in iot security current solutions and future challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "machine learning ddos detection for consumer internet of things devices"
    - "ai based two stage intrusion detection for software defined iot networks"
    - "machine intelligence techniques for next generation context aware wireless networks"
    - "securing the internet of things in the age of machine learning and software defined networking"
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "applications of deep learning and reinforcement learning to biological data"
    - "deep learning for iot big data and streaming analytics a survey"

Connected through hyperedge HE-3 (induced by source paper "a thorough study of lorawan performance under different parameter settings"):
  with members:
    - "employing p csma on a lora network simulator"
    - "uncoordinated access schemes for the iot approaches regulations and performance"
    - "does bidirectional traffic do more harm than good in lorawan based lpwa networks"
    - "analysis of latency and mac layer performance for class a lorawan"
    - "scalability analysis of large scale lorawan networks in ns 3"
    - "m2m massive access in lte rach performance evaluation in a smart city scenario"

Connected through hyperedge HE-4 (induced by source paper "towards a strategy for supporting the engineering of contemporary software systems"):
  with members:
    - "internet of things survey on security"
    - "towards a general software engineering methodology for the internet of things"
    - "enabling high level application development for the internet of things"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-5 (induced by source paper "internet of things enabled policing processes"):
  with members:
    - "data curation apis"
    - "big data and cross document coreference resolution current state and future opportunities"
    - "temporal provenance model tpm model and query language"

Connected through hyperedge HE-6 (induced by source paper "residual energy based cluster head selection in wsns for iot application"):
  with members:
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-7 (induced by source paper "deployment optimization of iot devices through attack graph analysis"):
  with members:
    - "all your bulbs are belong to us investigating the current state of security in connected lighting systems"

Connected through hyperedge HE-8 (induced by source paper "distill net application specific distillation of deep convolutional neural networks for resource constrained iot platforms"):
  with members:
    - "squeezenext hardware aware neural network design"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "in datacenter performance analysis of a tensor processing unit"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "condensenet an efficient densenet using learned group convolutions"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"
    - "cnndroid gpu accelerated execution of trained deep convolutional neural networks on android"

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.CY(Computers and Society)