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: "localization in long range ultra narrow band iot networks using rssi"
  Abstract: Internet of things wireless networking with long-range, low power and low throughput is raising as a new paradigm enabling to connect trillions of devices efficiently. In such networks with low power and bandwidth devices, localization becomes more challenging. In this work we take a closer look at the underlying aspects of received signal strength indicator (RSSI) based localization in UNB long-range IoT networks such as Sigfox. Firstly, the RSSI has been used for fingerprinting localization where RSSI measurements of GPS anchor nodes have been used as landmarks to classify other nodes into one of the GPS nodes classes. Through measurements we show that a location classification accuracy of 100% is achieved when the classes of nodes are isolated. When classes are approaching each other, our measurements show that we can still achieve an accuracy of 85%. Furthermore, when the density of the GPS nodes is increasing, we can rely on peer-to-peer triangulation and thus improve the possibility of localizing nodes with an error less than 20m from 20% to more than 60% of the nodes in our measurement scenario. 90% of the nodes is localized with an error of less than 50m in our experiment with non-optimized anchor node locations.

Connected through hyperedge HE-1 (induced by source paper "localization in ultra narrow band iot networks design guidelines and tradeoffs"):
  with members:
    - "energy constrained uav trajectory design for ground node localization"

Connected through hyperedge HE-2 (induced by source paper "tutorial on uav a blue sky view on wireless communication"):
  with members:
    - "cellular connectivity for uavs network modeling performance analysis and design guidelines"
    - "energy constrained uav trajectory design for ground node localization"
    - "comp in the sky uav placement and movement optimization for multi user communications"
    - "key technologies and system trade offs for detection and localization of amateur drones"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "aerial anchors positioning for reliable rss based outdoor localization in urban environments"
    - "an overview of 3gpp release 15 study on enhanced lte support for connected drones"
    - "ultra reliable uav communication using altitude and cooperation diversity"

Connected through hyperedge HE-3 (induced by source paper "key technologies and system trade offs for detection and localization of amateur drones"):
  with members:
    - "electrosense open and big spectrum data"
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "flight dynamics based recovery of a uav trajectory using ground cameras"
    - "deep architectures for modulation recognition"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "convolutional radio modulation recognition networks"

Connected through hyperedge HE-4 (induced by source paper "a survey of indoor localization systems and technologies"):
  with members:
    - "discriminative learning based smartphone indoor localization"
    - "mbps experimental acoustic through tissue communications meat comms"
    - "low power wide area networks an overview"
    - "ibeacon based proximity and indoor localization system"
    - "microlocation for internet of things equipped smart buildings"
    - "csi based fingerprinting for indoor localization a deep learning approach"
    - "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"

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.IT(Information Theory)