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: "downlink rate analysis for virtual cell based large scale distributed antenna systems"
  Abstract: Despite substantial rate gains achieved by joint transmission from a massive amount of geographically distributed antennas, the resulting computational cost and channel measurement overhead could be unaffordable for a large-scale distributed antenna system (DAS). A scalable signal processing framework is therefore highly desirable, which could be established based on the concept of virtual cell. In a virtual-cell based DAS, each user chooses a few neighboring base-station (BS) antennas to form its virtual cell, i.e, its own serving BS antenna set. In this paper, we focus on a downlink DAS with a large number of users and BS antennas uniformly distributed in a certain area, and aim to study the effect of the virtual cell size on the average user rate. Specifically, by assuming that maximum ratio transmission (MRT) is adopted in each user’s virtual cell, the achievable ergodic rate of each user is derived as an explicit function of the large-scale fading coefficients from all the users to their virtual cells, and an upper-bound of the average user rate is established, based on which a rule of thumb is developed for determining the optimal virtual cell size to maximize the average user rate. The analysis is further extended to consider multiple users grouped together and jointly served by their virtual cells using zero-forcing beamforming (ZFBF). In contrast to the no-grouping case where a small virtual cell size is preferred, it is shown that by grouping users with overlapped virtual cells, the average user rate can be significantly improved by increasing the virtual cell size, though at the cost of a higher signal processing complexity.

Connected through hyperedge HE-1 (induced by source paper "user centric joint transmission in virtual cell based ultra dense networks"):
  with members:
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "wireless communications in the era of big data"
    - "a stochastic geometry framework for analyzing pairwise cooperative cellular networks"
    - "spectral efficiency of dynamic coordinated beamforming a stochastic geometry approach"
    - "a tractable model for noncoherent joint transmission base station cooperation"

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)