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: "pulse shaping diversity to enhance throughput in ultra dense small cell networks"
  Abstract: Spatial multiplexing (SM) gains in multiple input multiple output (MIMO) cellular networks are limited when used in combination with ultra-dense small cell networks. This limitation is due to large spatial correlation among channel pairs. More specifically, it is due to i) line-of-sight (LOS) communication between user equipment (UE) and base station (BS) and ii) insufficient spacing between antenna elements. We propose to shape transmit signals at adjacent antennas with distinct interpolating filters which introduces pulse shaping diversity eventually leading to improved SINR and throughput at the UEs. In this technique, each antenna transmits its own data stream with a relative offset with respect to adjacent antenna. The delay which must be a fraction of symbol period is interpolated with the pulse shaped signal and generates a virtual MIMO channel that leads to improved diversity and SINR at the receiver. Note that non-integral sampling periods with inter-symbol interference (ISI) should be mitigated at the receiver. For this, we propose to use a fractionally spaced equalizer (FSE) designed based on the minimum mean squared error (MMSE) criterion. Simulation results show that for a 2×2 MIMO and with inter-site-distance (ISD) of 50 m, the median received SINR and throughput at the UE improves by a factor of 11 dB and 2×, respectively, which verifies that pulse shaping can overcome poor SM gains in ultra-dense small cell networks.

Connected through hyperedge HE-1 (induced by source paper "diversity pulse shaped transmission in ultra dense small cell networks"):
  with members:
    - "fronthauling for 5g lte u ultra dense cloud small cell networks"
    - "performance impact of los and nlos transmissions in dense cellular networks"
    - "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"
    - "study on scheduling techniques for ultra dense small cell 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)