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: "on the peak to average power ratio reduction problem for orthogonal transmission schemes"
  Abstract: High peak values of transmission signals in wireless communication systems lead to wasteful energy consumption and out-of-band radiation. However, reducing peak values generally comes at the cost some other resource. We provide a theoretical contribution towards understanding the relationship between peak value reduction and the resulting cost in information rates. In particular, we address the relationship between peak values and the proportion of transmission signals allocated for information transmission when using a strategy known as tone reservation. We show that when using tone reservation in both OFDM and DS-CDMA systems, if a Peak-to-Average Power Ratio criterion is always satisfied, then the proportion of transmission signals that may be allocated for information transmission must tend to zero. We investigate properties of these two systems for sets of both finite and infinite cardinalities. We present properties that OFDM and DS-CDMA share in common as well as ways in which they fundamentally differ.

Connected through hyperedge HE-1 (induced by source paper "capacity and degree of freedom of ofdm channels with amplitude constraint"):
  with members:
    - "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels"
    - "the papr problem in ofdm transmission new directions for a long lasting problem"

Connected through hyperedge HE-2 (induced by source paper "the papr problem in ofdm transmission new directions for a long lasting problem"):
  with members:
    - "peak reduction and clipping mitigation in ofdm by augmented compressive sensing"
    - "expected supremum of a random linear combination of shifted kernels"
    - "analysis of alternative metrics for the papr problem in ofdm transmission"
    - "on the concentration of the crest factor for ofdm signals"
    - "clipping noise cancellation for ofdm and ofdma systems using compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "on cosets of the generalized first order reed muller code with low pmepr"

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)