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: "reliable computation by formulas of noisy and gates and noiseless xor gates with applications to quantum mechanics"
  Abstract: It has long been known that certain super-quantum nonlocal correlations cause communication complexity to become trivial, and it has been conjectured that a statement like ``communication complexity is not trivial'' may provide an intuitive information-theoretic axiom for quantum mechanics. With this conjecture in mind, we consider the task of trivializing communication complexity using weaker, but still super-quantum, nonlocal correlations. The best prior construction [Brassard et al., 2006] depends upon a positive result for reliable computation by noisy gates, in particular formulas of $\epsilon$-noisy AND gates and noise-free XOR gates. We provide a complementary and tight negative result, which implies that the approach of Brassard et al. cannot be improved. More precisely, we determine that the threshold for reliable computation in this model is $\epsilon = 1/6$, in the sense that reliable computation is possible for any $\epsilon < 1/6$ and not possible for any $\epsilon \geq 1/6$. This appears to be the first exact threshold for a circuit model that includes noise-free binary gates.

Connected through hyperedge HE-1 (induced by source paper "broadcasting on random directed acyclic graphs"):
  with members:
    - "noise thresholds for amplification quantum foundations from classical fault tolerant computation"
    - "strong data processing inequalities for channels and bayesian networks"
    - "dissipation of information in channels with input constraints"
    - "reconstruction and clustering in random constraint satisfaction problems"
    - "noise threshold for universality of 2 input gates"

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)