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: "integrated millimeter wave and sub 6 ghz wireless networks a roadmap for ultra reliable low latency communications"
  Abstract: Emerging wireless services such as augmented reality require next-generation wireless networks to support ultra-reliable and low-latency communication (URLLC), while also guaranteeing high data rates. Existing wireless networks that solely rely on the scarce sub-6 GHz, microwave ($\mu$W) frequency bands will be unable to meet the low-latency, high capacity requirements of future wireless services. Meanwhile, operating at high-frequency millimeter wave (mmWave) bands is seen as an attractive solution, primarily due to the bandwidth availability and possibility of large-scale multi-antenna communication. However, even though leveraging the large bandwidth at mmWave frequencies can potentially boost the wireless capacity and reduce the transmission delay for low-latency applications, mmWave communication is inherently unreliable due to its susceptibility to blockage, high path loss, and channel uncertainty. Hence, to provide URLLC and high-speed wireless access, it is desirable to seamlessly integrate the reliability of $\mu$W networks with the high capacity of mmWave networks. To this end, in this paper, the first comprehensive tutorial for integrated mmWave-$\mu$W communications is introduced. This envisioned integrated design will enable wireless networks to achieve URLLC along with high data rates by leveraging the best of two worlds: reliable, long-range communications at the $\mu$W bands and directional high-speed communications at the mmWave frequencies. To achieve this goal, key solution concepts are developed that include new architectures for the radio interface, URLLC-aware frame structure and resource allocation methods along with learning techniques, as well as mobility management, to realize the potential of integrated mmWave-$\mu$W communications. The opportunities and challenges of each proposed scheme are discussed and key results are presented.

Connected through hyperedge HE-1 (induced by source paper "classification algorithms for semi blind uplink downlink decoupling in sub 6 ghz mmwave 5g networks"):
  with members:
    - "csi based outdoor localization for massive mimo experiments with a learning approach"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "rician k factor based analysis of xlos service probability in 5g outdoor ultra dense networks"
    - "deep q learning for self organizing networks fault management and radio performance improvement"
    - "downlink cellular network analysis with los nlos propagation and elevated base stations"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-2 (induced by source paper "optimized path planning for inspection by unmanned aerial vehicles swarm with energy constraints"):
  with members:
    - "average peak age of information minimization in uav assisted iot networks"
    - "age optimal trajectory planning for uav assisted data collection"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "energy efficient uav communication with trajectory optimization"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"

Connected through hyperedge HE-3 (induced by source paper "ultra reliable low latency vehicle to infrastructure wireless communications with edge computing"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "spatio temporal motifs for optimized vehicle to vehicle v2v communications"
    - "high reliability and low latency for vehicular networks challenges and solutions"
    - "collaborative mobile edge computing in 5g networks new paradigms scenarios and challenges"
    - "user association for load balancing in heterogeneous cellular networks"

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)