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: "lossy compression of discrete sources via the viterbi algorithm"
  Abstract: We present a new lossy compressor for finite-alphabet sources. For coding a sequence xn, the encoder starts by assigning a certain cost to each possible reconstruction sequence. It then finds the one that minimizes this cost and describes it losslessly to the decoder via a universal lossless compressor. The cost of each sequence is a linear combination of its distance from the sequence xn and a linear function of its kth order empirical distribution. The structure of the cost function allows the encoder to employ the Viterbi algorithm to find the sequence with minimum cost. We identify a choice of the coefficients used in the cost function which ensures that the algorithm universally achieves the optimum rate-distortion performance for any stationary ergodic source, in the limit of large , provided that increases as o(log n). Iterative techniques for approximating the coefficients, which alleviate the computational burden of finding the optimal coefficients, are proposed and studied.

Connected through hyperedge HE-1 (induced by source paper "theoretical links between universal and bayesian compressed sensing algorithms"):
  with members:
    - "an efficient algorithm for compression based compressed sensing"
    - "on the information dimension of stochastic processes"
    - "compression based compressed sensing"
    - "new approach to bayesian high dimensional linear regression"
    - "from denoising to compressed sensing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "recovery from linear measurements with complexity matching universal signal estimation"

Connected through hyperedge HE-2 (induced by source paper "new approach to bayesian high dimensional linear regression"):
  with members:
    - "universal compressed sensing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "iterative hard thresholding for compressed sensing"
    - "randomly spread cdma asymptotics via statistical physics"

Connected through hyperedge HE-3 (induced by source paper "dynamic compression transmission for energy harvesting multihop networks with correlated sources"):
  with members:
    - "sparse regression codes for multi terminal source and channel coding"
    - "worst case source for distributed compression with quadratic distortion"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"
    - "a general framework for the optimization of energy harvesting communication systems with battery imperfections"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"
    - "optimal energy management policies for energy harvesting sensor nodes"

Connected through hyperedge HE-4 (induced by source paper "from compression to compressed sensing"):
  with members:
    - "minimum complexity pursuit for universal compressed sensing"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "minimum complexity pursuit stability analysis"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "stable restoration and separation of approximately sparse signals"
    - "optimal phase transitions in compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "compression with actions"):
  with members:
    - "estimation with a helper who knows the interference"
    - "lossy computing of correlated sources with fractional sampling"
    - "information embedding meets distributed control"

Connected through hyperedge HE-6 (induced by source paper "optimal lempel ziv based lossy compression for memoryless data how to make the right mistakes"):
  with members:
    - "lower bounds on the rate distortion function of ldgm codes"
    - "low density codes achieve the rate distortion bound"

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)