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: "stochastic geometry based uplink analysis of massive mimo systems with fractional pilot reuse"
  Abstract: In this work, we analyze the performance of the uplink (UL) of a massive MIMO network considering an asymptotically large number of antennas at base stations (BSs). We model the locations of BSs as a homogeneous Poisson point process (PPP) and assume that their service regions are limited to their respective Poisson-Voronoi cells (PVCs). Further, for each PVC, based on a threshold radius, we model the cell center (CC) region as the Johnson-Mehl (JM) cell of its BS while rest of the PVC is deemed as the cell edge (CE) region. The CC and CE users are located uniformly at random independently of each other in the JM cell and CE region, respectively. In addition, we consider a fractional pilot reuse (FPR) scheme where two different sets of pilot sequences are used for CC and CE users with the objective of reducing the interference due to pilot contamination for CE users. Based on the above system model, we derive analytical expressions for the UL signal-to-interference-and-noise ratio (SINR) coverage probability and average spectral efficiency (SE) for randomly selected CC and CE users. In addition, we present an approximate expression for the average cell SE. One of the key intermediate results in our analysis is the approximate but accurate characterization of the distributions of the CC and CE areas of a typical cell. Another key intermediate step is the accurate characterization of the pair correlation functions of the point processes formed by the interfering CC and CE users that subsequently enables the coverage probability analysis. From our system analysis, we present a partitioning rule for the number of pilot sequences to be used for CC and CE users as a function of threshold radius that improves the average CE user SE while achieving similar CC user SE with respect to unity pilot reuse.

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"
    - "accuracy of distance based ranking of users in the analysis of noma systems"
    - "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 "non coherent joint transmission in poisson cellular networks under pilot contamination"):
  with members:
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "channel hardening and favorable propagation in cell free massive mimo with stochastic geometry"
    - "cell free massive mimo versus small cells"
    - "deploying dense networks for maximal energy efficiency small cells meet massive mimo"
    - "analyzing uplink sinr and rate in massive mimo systems using stochastic geometry"
    - "on stochastic geometry modeling of cellular uplink transmission with truncated channel inversion power control"
    - "a tractable model for noncoherent joint transmission base station cooperation"
    - "a tractable approach to coverage and rate in 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)