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 capacity of noisy computations"
  Abstract: This paper presents an analysis of the concept of capacity for noisy com- putations, i.e. functions implemented by unreliable or random devices. An information theoretic model of noisy computation of a perfect function f (measurable function between sequence spaces) thanks to an unreliable device (random channel) F is given: a noisy computation is a product fxF of channels. A model of reliable computation based on input encoding and output decoding is also proposed. These models extend those of noisy communication channel and of reliable communication through a noisy channel. The capacity of a noisy computation is defined and justified by a coding theorem and a converse. Under some constraints on the encoding process, capacity is the upper bound of input rates allowing reliable computation, i.e. decodability of noisy outputs into expected outputs. These results hold when the one-sided random processes under concern are asymptotic mean stationary (AMS) and ergodic. In addition, some characterizations of AMS and ergodic noisy computations are given based on stability properties of the perfect function f and of the random channel F. These results are derived from the more general framework of channel products. Finally, a way to apply the noisy and reliable computation models to cases where the perfect function f is defined according to a formal computational model is proposed.

Connected through hyperedge HE-1 (induced by source paper "viral turing machines computation from noise and combinatorial hierarchies"):
  with members:
    - "molecular computers"
    - "graphic lambda calculus"
    - "info computationalism and philosophical aspects of research in information sciences"

Connected through hyperedge HE-2 (induced by source paper "computing linear transformations with unreliable components"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "information friction and its implications on minimum energy required for communication"
    - "density evolution and functional threshold for the noisy min sum decoder"
    - "density evolution for min sum decoding of ldpc codes under unreliable message storage"
    - "energy consumption of vlsi decoders"
    - "coding with encoding uncertainty"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"

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)