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: "accuracy of distance based ranking of users in the analysis of noma systems"
  Abstract: We characterize the accuracy of analyzing the performance of a NOMA system where users are ranked according to their distances instead of instantaneous channel gains, i.e., product of distance-based path-loss and fading channel gains. Distance-based ranking is analytically tractable and can lead to important insights. However, it may not be appropriate in a multipath fading environment where a near user suffers from severe fading while a far user experiences weak fading. Since the ranking of users in a NOMA system has a direct impact on coverage probability analysis, impact of the traditional distance-based ranking, as opposed to instantaneous signal power-based ranking, needs to be understood. This will enable us to identify scenarios where distance-based ranking, which is easier to implement compared to instantaneous signal power-based ranking, is acceptable for system performance analysis. To this end, in this paper, we derive the probability of the event when distance-based ranking yields the same results as instantaneous signal power-based ranking, which is referred to as the accuracy probability. We characterize the probability of accuracy considering Nakagami-m fading channels and three different spatial distribution models of user locations in NOMA. We illustrate the impact of accuracy probability on uplink and downlink coverage probability.

Connected through hyperedge HE-1 (induced by source paper "downlink analysis of noma enabled cellular networks with 3gpp inspired user ranking"):
  with members:
    - "downlink analysis for the typical cell in poisson cellular networks"
    - "noma in the uplink delay analysis with imperfect csi and finite length coding"
    - "meta distribution of downlink non orthogonal multiple access noma in poisson networks"
    - "downlink non orthogonal multiple access noma in poisson networks"
    - "stochastic geometry based uplink analysis of massive mimo systems with fractional pilot reuse"
    - "non orthogonal multiple access in large scale heterogeneous networks"
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"
    - "on optimal power allocation for downlink non orthogonal multiple access systems"

Connected through hyperedge HE-2 (induced by source paper "uplink noma for cellular connected uav impact of uav trajectories and altitude"):
  with members:
    - "3gpp inspired stochastic geometry based mobility model for a drone cellular network"
    - "exploiting noma for multi beam uav communication in cellular uplink"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "underlay drone cell for temporary events impact of drone height and aerial channel environments"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "uplink cooperative noma for cellular connected uav"
    - "survey on uav cellular communications practical aspects standardization advancements regulation and security challenges"

Connected through hyperedge HE-3 (induced by source paper "on coverage probability in uplink noma with instantaneous signal power based user ranking"):
  with members:
    - "user point processes in cellular networks"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"

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)