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 channel estimation via phase retrieval"
  Abstract: Pilot-aided channel estimation is nowadays a standard component in each wireless receiver enabling coherent transmission of complex-valued constellations, only affected by noise and interference. Whenever these disturbances are sufficiently small and long data frames are used, high data rates can be achieved and the resource overhead due to the pilots vanishes asymptotically. On the other, it is expected that for the next generation of mobile networks not only data rate is in the main focus but also low latency, short and sporadic messages, massive connectivity, distributed & adhoc processing and robustness with respect to asynchronism. Therefore a review of several well-established principles in communication has been started already. A particular implication when using complex-valued pilots is that these values have to be known at the receiver and therefore these resources can not be used simultaneously for user data. For an OFDM-like multicarrier scheme this means that pilot tones (usually placed equidistantly according to the Nyquist theorem) are allocated with globally known amplitudes and phases to reconstruct the channel impulse response. Phases are designed and allocated globally which is in contrast to a distributed infrastructure. In this work we present therefore a new phaseless pilot scheme where only pilot amplitudes need to be known at the receiver, i.e., phases are available again and can be used for various other purposes. The idea is based on a phase retrieval result for symmetrized and zero-padded magnitude Fourier measurements obtained by two of the authors. The phases on the pilot tones can now be used to carry additional user-specific data or compensate for other signal characteristics, like the PAPR.

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)