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: "signal processing in large systems a new paradigm"
  Abstract: For a long time, detection and parameter estimation methods for signal processing have relied on asymptotic statistics as the number $n$ of observations of a population grows large comparatively to the population size $N$, i.e. $n/N\to \infty$. Modern technological and societal advances now demand the study of sometimes extremely large populations and simultaneously require fast signal processing due to accelerated system dynamics. This results in not-so-large practical ratios $n/N$, sometimes even smaller than one. A disruptive change in classical signal processing methods has therefore been initiated in the past ten years, mostly spurred by the field of large dimensional random matrix theory. The early works in random matrix theory for signal processing applications are however scarce and highly technical. This tutorial provides an accessible methodological introduction to the modern tools of random matrix theory and to the signal processing methods derived from them, with an emphasis on simple illustrative examples.

Connected through hyperedge HE-1 (induced by source paper "enhanced group sparse beamforming for green cloud ran a random matrix approach"):
  with members:
    - "layered group sparse beamforming for cache enabled green wireless networks"
    - "energy efficiency of downlink transmission strategies for cloud radio access networks"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "wireless communications in the era of big data"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "heterogeneous cellular networks using wireless backhaul fast admission control and large system analysis"
    - "robust group sparse beamforming for multicast green cloud ran with imperfect csi"

Connected through hyperedge HE-2 (induced by source paper "highly efficient hierarchical online nonlinear regression using second order methods"):
  with members:
    - "a comprehensive approach to universal piecewise nonlinear regression based on trees"

Connected through hyperedge HE-3 (induced by source paper "pilot distribution optimization and power control in multi cellular large scale mimo systems"):
  with members:
    - "power allocation schemes for multicell massive mimo systems"
    - "noncoherent trellis coded quantization a practical limited feedback technique for massive mimo systems"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-4 (induced by source paper "backing off from infinity performance bounds via concentration of spectral measure for random mimo channels"):
  with members:
    - "massive mimo for next generation wireless systems"
    - "bounds on the second order coding rate of the mimo rayleigh block fading channel"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "on the distribution of mimo mutual information an in depth painleve based characterization"
    - "outage capacity for the optical mimo channel"
    - "network mimo with linear zero forcing beamforming large system analysis impact of channel estimation and reduced complexity scheduling"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"

Connected through hyperedge HE-5 (induced by source paper "ber analysis of multi cellular mimo systems with increasing number of bs antennas"):
  with members:
    - "noncoherent trellis coded quantization a practical limited feedback technique for massive mimo systems"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "pilot contamination and precoding in multi cell tdd systems"

Connected through hyperedge HE-6 (induced by source paper "near optimal sensor placement for linear inverse problems"):
  with members:
    - "online distributed sensor selection"

Connected through hyperedge HE-7 (induced by source paper "uplink performance analysis of multicell mu mimo systems with zf receivers"):
  with members:
    - "massive mimo how many antennas do we need"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "downlink interference alignment"
    - "pilot contamination and precoding in multi cell tdd systems"
    - "mimo diversity in the presence of double scattering"

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)