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: "helper heterogeneous efficient low power radio for enabling ad hoc emergency public safety networks"
  Abstract: Abstract   Natural and man-made disasters have been causing destruction and distress to humanity all over the world. In these scenarios, communication infrastructures are the most affected entities making the rescue and emergency response operations extremely challenging. This invokes a need to equip the affected people and the emergency responders with the ability to rapidly set up and use independent means of communication. Therefore, in this work, we present a complete end-to-end solution that can connect survivors of a disaster with each other and the authorities using a completely self-sufficient ad hoc network that can be setup rapidly. Accordingly, we develop a Heterogeneous Efficient Low Power Radio (HELPER) that acts as a WiFi (Wireless Fidelity) access point for end-users to connect using website application developed by us. These HELPERs then coordinate with each other to form a LoRa based ad hoc network. To this end, we propose a novel cross-layer optimized distributed energy-efficient routing (SEEK) algorithm that aims to maximize the network lifetime. This aspect is critical especially in energy constrained scenarios after a disaster.  To prove the feasibility of the solutions, we prototype the HELPER using WiFi enabled Raspberry Pi and LoRa module that is configured to run using Li-ion batteries. We implement the required cross-layer protocol stack along with the SEEK routing algorithm and develop a website application that an end-user can avail to connect using any device such as smartphones, tablets, laptops etc. We have conducted demonstrations to establish the feasibility of exchanging of text messages over the HELPER network, live map updates, ability to send distress messages (like 9-1-1 calls) to authorities. In the context of authorities, we have shown how they can leverage this technology to remotely monitor the connectivity of the affected area, alert users of imminent dangers and share resource (water, food, first aid) availability information. We have also conducted an extensive numerical evaluation of SEEK algorithm against a greedy geographical routing algorithm using the HELPER testbed. Results showed up to 53% improvement in network lifetime and up to 28% improvement in throughput. Overall, we hope this technology will become instrumental in improving the efficiency and effectiveness of public safety activities.

Connected through hyperedge HE-1 (induced by source paper "machine learning for wireless communications in the internet of things a comprehensive survey"):
  with members:
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "over the air deep learning based radio signal classification"
    - "a non parametric multi stage learning framework for cognitive spectrum access in iot networks"
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "cognitive non orthogonal multiple access with cooperative relaying a new wireless frontier for 5g spectrum sharing"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "fog computing survey of trends architectures requirements and research directions"
    - "an introduction to deep learning for the physical layer"

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)