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: "ofdm without cp in massive mimo"
  Abstract: We study the possibility of eliminating the redundant cyclic prefix (CP) of orthogonal frequency division multiplexing (OFDM) in massive multiple-input multiple-output (MIMO) systems. The absence of CP increases the bandwidth efficiency in expense of intersymbol interference (ISI) and intercarrier interference (ICI). It is known that in massive MIMO, different types of interferences vanish as the number of base station (BS) antennas tends to infinity. To investigate if the channel distortions in the absence of CP fade away, we first study the performance of the standard frequency-domain maximum ratio combining (MRC) receiver. Our analysis reveals that in this receiver there always remains some residual interference leading to saturation of signal-to-interference-plus-noise ratio (SINR). In order to resolve this saturation problem, we propose a receiver structure based on time-reversal equalization combined with a zero-forcing multiuser detector. A detailed mathematical analysis of this receiver, including a lower bound for its asymptotic achievable information rate, is presented. We evaluate our theoretical results through conducting numerical analysis where we compare the tightness of our closed-form expressions against the simulation results. Finally, our simulation results reveal that, in massive MIMO systems, the absence of CP in OFDM does not incur any noticeable performance loss.

Connected through hyperedge HE-1 (induced by source paper "artificial intelligence aided receiver for a cp free ofdm system design simulation and experimental test"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "deep learning in physical layer communications"
    - "probabilistic mimo symbol detection with expectation consistency approximate inference"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "model driven deep learning for physical layer communications"
    - "a model driven deep learning network for mimo detection"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-2 (induced by source paper "channel shortening by large multiantenna precoding in ofdm"):
  with members:
    - "time frequency selective mimo precoding for ofdm with insufficient cp"
    - "performance analysis of massive mimo for cell boundary users"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-3 (induced by source paper "filter bank multicarrier in massive mimo analysis and channel equalization"):
  with members:
    - "prototype filter design for fbmc in massive mimo channels"
    - "cell free massive mimo versus small cells"
    - "frequency spreading equalization in multicarrier massive mimo"
    - "impact of timing and frequency offsets on multicarrier waveform candidates for 5g"
    - "filter bank multicarrier for massive mimo"
    - "pilot decontamination in cmt based massive mimo networks"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-4 (induced by source paper "the effect of diversity combining on isi in massive mimo"):
  with members:
    - "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-5 (induced by source paper "time frequency selective mimo precoding for ofdm with insufficient cp"):
  with members:
    - "energy and spectral efficiency of very large multiuser mimo systems"

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)