human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "patient centric hetnets powered by machine learning and big data analytics for 6g networks"):
  Abstract: Having a cognitive and self-optimizing network that proactively adapts not only to channel conditions, but also according to its users needs can be one of the highest forthcoming priorities of future 6G Heterogeneous Networks (HetNets). In this paper, we introduce an interdisciplinary approach linking the concepts of e-healthcare, priority, big data analytics (BDA) and radio resource optimization in a multi-tier 5G network. We employ three machine learning (ML) algorithms, namely, naive Bayesian (NB) classifier, logistic regression (LR), and decision tree (DT), working as an ensemble system to analyze historical medical records of stroke out-patients (OPs) and readings from body-attached internet-of-things (IoT) sensors to predict the likelihood of an imminent stroke. We convert the stroke likelihood into a risk factor functioning as a priority in a mixed integer linear programming (MILP) optimization model. Hence, the task is to optimally allocate physical resource blocks (PRBs) to HetNet users while prioritizing OPs by granting them high gain PRBs according to the severity of their medical state. Thus, empowering the OPs to send their critical data to their healthcare provider with minimized delay. To that end, two optimization approaches are proposed, a weighted sum rate maximization (WSRMax) approach and a proportional fairness (PF) approach. The proposed approaches increased the OPs average signal to interference plus noise (SINR) by 57% and 95%, respectively. The WSRMax approach increased the system total SINR to a level higher than that of the PF approach, nevertheless, the PF approach yielded higher SINRs for the OPs, better fairness and a lower margin of error.

Includes members:
  Member "using machine learning and big data analytics to prioritize outpatients in hetnets":
    also connected through other hyperedges:
      (none)
  Member "service embedding in iot networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "energy efficient distributed processing for iot")
  Member "big data analytics for wireless and wired network design a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "energy efficient virtual machines placement over cloud fog network architecture")
  Member "patient centric cellular networks optimization using big data analytics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficient distributed processing for iot")
  Member "energy efficient iot virtualization framework with peer to peer networking and processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficient distributed processing for iot")
  Member "human in the loop wireless communications machine learning and brain aware resource management":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "risk based optimization of virtual reality over terahertz reconfigurable intelligent surfaces")
  Member "optimized energy aware 5g network function virtualization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "energy efficient distributed processing for iot")
  Member "stacking classifiers for anti spam filtering of e mail":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "funnelling a new ensemble method for heterogeneous transfer learning and its application to cross lingual text classification")

Question: Please tell me which class the source paper 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)