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: "scaling laws for ergodic spectral efficiency in mimo poisson networks"
  Abstract: In this paper, we examine the benefits of multiple antenna communication in random wireless networks, the topology of which is modeled by stochastic geometry. The setting is that of the Poisson bipolar model introduced in [1], which is a natural model for ad-hoc and device-to-device (D2D) networks. The primary finding is that, with knowledge of channel state information between a receiver and its associated transmitter, by zero-forcing successive interference cancellation, and for appropriate antenna configurations, the ergodic spectral efficiency can be made to scale linearly with both 1) the minimum of the number of transmit and receive antennas, 2) the density of nodes and 3) the path-loss exponent. This linear gain is achieved by using the transmit antennas to send multiple data streams (e.g. through an open-loop transmission method) and by exploiting the receive antennas to cancel interference. Furthermore, when a receiver is able to learn channel state information from a certain number of near interferers, higher scaling gains can be achieved when using a successive interference cancellation method. A major implication of the derived scaling laws is that spatial multiplexing transmission methods are essential for obtaining better and eventually optimal scaling laws in multiple antenna random wireless networks. Simulation results support this analysis.

Connected through hyperedge HE-1 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks"):
  with members:
    - "scaling laws of dense multi antenna cellular networks"
    - "sinr and throughput of dense cellular networks with stretched exponential path loss"
    - "a unified asymptotic analysis of area spectral efficiency in ultradense cellular networks"
    - "performance limits of network densification"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "performance impact of base station antenna heights in dense cellular networks"
    - "a primer on cellular network analysis using stochastic geometry"
    - "cooperative base station coloring for pair wise multi cell coordination"

Connected through hyperedge HE-2 (induced by source paper "ergodic spectral efficiency in mimo cellular networks"):
  with members:
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "enriched k tier hetnet model to enable the analysis of user centric small cell deployments"
    - "wireless network signals with moderately correlated shadowing still appear poisson"
    - "gaussian approximation for the downlink interference in heterogeneous cellular networks"
    - "analytical tractability of hexagonal network model with random user location"
    - "asymptotics and approximation of the sir distribution in general cellular networks"
    - "an analytical framework for device to device communication in cellular networks"
    - "success probability and area spectral efficiency in multiuser mimo hetnets"

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)