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: "randomness and non determinism"
  Abstract: Exponentiation makes the difference between the bit-size of this line and the number (<< 2^{300}) of particles in the known Universe. The expulsion of exponential time algorithms from Computer Theory in the 60's broke its umbilical cord from Mathematical Logic. It created a deep gap between deterministic computation and -- formerly its unremarkable tools -- randomness and non-determinism. Little did we learn in the past decades about the power of either of these two basic "freedoms" of computation, but some vague pattern is emerging in relationships between them. The pattern of similar techniques instrumental for quite different results in this area seems even more interesting. Ideas like multilinear and low-degree multivariate polynomials, Fourier transformation over low-periodic groups seem very illuminating. The talk surveyed some recent results. One of them, given in a stronger form than previously published, is described below.

Connected through hyperedge HE-1 (induced by source paper "sample optimal average case sparse fourier transform in two dimensions"):
  with members:
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "nearly optimal sparse fourier transform"
    - "1 eps approximate sparse recovery"

Connected through hyperedge HE-2 (induced by source paper "learning using local membership queries"):
  with members:
    - "learning dnf expressions from fourier spectrum"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"
    - "noise tolerant learning the parity problem and the statistical query model"

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.CC(Computational Complexity)