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: "sensing as a service and big data"
  Abstract: Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. Data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. This paper discusses emerging Internet of Things (IoT) architecture, large scale sensor network applications, federating sensor networks, sensor data and related context capturing techniques, challenges in cloud-based management, storing, archiving and processing of sensor data.

Connected through hyperedge HE-1 (induced by source paper "big data in iot systems"):
  with members:
    - "decentralized computation offloading game for mobile cloud computing"
    - "unique sense smart computing prototype for industry 4 0 revolution with iot and bigdata implementation model"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "application of wireless sensor networks for indoor temperature regulation"
    - "intriguing properties of neural networks"
    - "representation learning a review and new perspectives"
    - "internet of things iot a vision architectural elements and future directions"
    - "from machine learning to machine reasoning"

Connected through hyperedge HE-2 (induced by source paper "end to end learning from spectrum data a deep learning approach for wireless signal identification in spectrum monitoring applications"):
  with members:
    - "electrosense open and big spectrum data"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "spectrum monitoring for radar bands using deep convolutional neural networks"
    - "wireless interference identification with convolutional neural networks"
    - "hardware accelerated sdr platform for adaptive air interfaces"
    - "will sdn be part of 5g"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"

Connected through hyperedge HE-3 (induced by source paper "fog computing for sustainable smart cities a survey"):
  with members:
    - "fog networking an overview on research opportunities"
    - "a survey of systems for massive stream analytics"
    - "cloudrone micro clouds in the sky"
    - "applying seamful design in location based mobile museum applications"
    - "fog data enhancing telehealth big data through fog computing"
    - "energy efficient location and activity aware on demand mobile distributed sensing platform for sensing as a service in iot clouds"
    - "big data privacy in the internet of things era"
    - "the emerging internet of things marketplace from an industrial perspective a survey"

Connected through hyperedge HE-4 (induced by source paper "estimation of graphlet statistics"):
  with members:
    - "an almost tight distributed algorithm for computing single source shortest paths"
    - "role discovery in networks"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph sample and hold a framework for big graph analytics"
    - "graph based anomaly detection and description a survey"
    - "colorful triangle counting and a mapreduce implementation"

Connected through hyperedge HE-5 (induced by source paper "big data model simulation on a graph database for surveillance in wireless multimedia sensor networks"):
  with members:
    - "sensor discovery and configuration framework for the internet of things paradigm"
    - "nosql database new era of databases for big data analytics classification characteristics and comparison"
    - "sensing as a service model for smart cities supported by internet of things"

Connected through hyperedge HE-6 (induced by source paper "a hybrid approach for data analytics for internet of things"):
  with members:
    - "city data fusion sensor data fusion in the internet of things"
    - "the emerging internet of things marketplace from an industrial perspective a survey"

Connected through hyperedge HE-7 (induced by source paper "the role of cloud of things in smart cities"):
  with members:
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-8 (induced by source paper "a knowledge based resource discovery for internet of things"):
  with members:
    - "context aware computing for the internet of things a survey"
    - "context aware sensor search selection and ranking model for internet of things middleware"
    - "context aware sensor configuration model for internet of things"
    - "sensing as a service model for smart cities supported by internet of things"
    - "internet of things iot a vision architectural elements and future directions"
    - "ca4iot context awareness for internet of things"

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)