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: "life above threshold from list decoding to area theorem and mse"
  Abstract: We consider communication over memoryless channels using low-density parity-check code ensembles above the iterative (belief propagation) threshold. What is the computational complexity of decoding (i.e., of reconstructing all the typical input codewords for a given channel output) in this regime? We define an algorithm accomplishing this task and analyze its typical performance. The behavior of the new algorithm can be expressed in purely information-theoretical terms. Its analysis provides an alternative proof of the area theorem for the binary erasure channel. Finally, we explain how the area theorem is generalized to arbitrary memoryless channels. We note that the recently discovered relation between mutual information and minimal square error is an instance of the area theorem in the setting of Gaussian channels.

Connected through hyperedge HE-1 (induced by source paper "low density majority codes and the problem of graceful degradation"):
  with members:
    - "a lower bound on the expected distortion of joint source channel coding"
    - "low complexity concatenated ldpc staircase codes"
    - "comparison of channels criteria for domination by a symmetric channel"
    - "deep joint source channel coding for wireless image transmission"
    - "strong data processing inequalities for channels and bayesian networks"
    - "a source channel separation theorem with application to the source broadcast problem"
    - "protograph based raptor like ldpc codes"
    - "intermediate performance of rateless codes"

Connected through hyperedge HE-2 (induced by source paper "asymptotic mutual information for the two groups stochastic block model"):
  with members:
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "spectral detection in the censored block model"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "information theoretically optimal sparse pca"
    - "near optimal joint object matching via convex relaxation"
    - "accurate community detection in the stochastic block model via spectral algorithms"
    - "tight error bounds for structured prediction"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"

Connected through hyperedge HE-3 (induced by source paper "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"):
  with members:
    - "threshold saturation on bms channels via spatial coupling"
    - "new families of ldpc block codes formed by terminating irregular protograph based ldpc convolutional codes"
    - "asymptotically good ldpc convolutional codes based on protographs"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "the generalized area theorem and some of its consequences"
    - "terminated ldpc convolutional codes with thresholds close to capacity"

Connected through hyperedge HE-4 (induced by source paper "optimum estimation via gradients of partition functions and information measures a statistical mechanical perspective"):
  with members:
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "threshold effects in parameter estimation as phase transitions in statistical mechanics"
    - "rate distortion function via minimum mean square error estimation"
    - "how could the replica method improve accuracy of performance assessment of channel coding"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-5 (induced by source paper "mutual information relative entropy and estimation in the poisson channel"):
  with members:
    - "optimum estimation via gradients of partition functions and information measures a statistical mechanical perspective"
    - "the generalized area theorem and some of its consequences"
    - "randomly spread cdma asymptotics via statistical physics"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-6 (induced by source paper "the capacity of finite state channels in the high noise regime"):
  with members:
    - "derivatives of entropy rate in special families of hidden markov chains"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "the entropy of a binary hidden markov process"

Connected through hyperedge HE-7 (induced by source paper "low complexity ldpc codes with near optimum performance over the bec"):
  with members:
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"

Connected through hyperedge HE-8 (induced by source paper "solving constraint satisfaction problems through belief propagation guided decimation"):
  with members:
    - "can rare sat formulae be easily recognized on the efficiency of message passing algorithms for k sat at large clause to variable ratios"
    - "counting good truth assignments of random k sat formulae"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes"
    - "threshold values of random k sat from the cavity method"
    - "survey propagation an algorithm for satisfiability"

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)