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: "rate distortion via markov chain monte carlo"
  Abstract: We propose a new approach to lossy source coding. The idea is to sample a reconstruction sequence from a Boltzmann distribution associated with an energy function that incorporates the distortion between the source and reconstruction, the compressibility of the reconstruction, and the point sought on the rate-distortion curve. To sample from this distribution, we use a heat bath algorithm: Starting from an initial candidate reconstruction (say the original source sequence), at every iteration, an index i is chosen and the ith sequence component is replaced by drawing from the conditional probability distribution for that component given all the rest. At the end of this process, the encoder losslessly conveys the reconstruction to the decoder using universal lossless compression. An appropriate choice of the energy function leads to an algorithm whose complexity, in each iteration, is independent of the sequence length and only linearly dependent on a certain context parameter (which grows sub-logarithmically with the sequence length). The algorithm is universal: for any stationary ergodic source, it achieves the optimal rate-distortion performance in the limits of large number of iterations and sequence length. Initial experimentation shows promising performance in practice.

Connected through hyperedge HE-1 (induced by source paper "sparse regression codes"):
  with members:
    - "the error probability of sparse superposition codes with approximate message passing decoding"
    - "sparcs for unsourced random access"
    - "spatially coupled sparse regression codes design and state evolution analysis"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "the rate distortion function and excess distortion exponent of sparse regression codes with optimal encoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"

Connected through hyperedge HE-2 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "performance trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "phase transitions in sparse pca"
    - "compressive imaging via approximate message passing with image denoising"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "mismatched estimation in large linear systems"

Connected through hyperedge HE-3 (induced by source paper "lossy compression via sparse linear regression computationally efficient encoding and decoding"):
  with members:
    - "lossy compression via sparse linear regression performance under minimum distance encoding"
    - "sparse regression codes for multi terminal source and channel coding"
    - "fast sparse superposition codes have exponentially small error probability for r l c"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "the dispersion of lossy source coding"
    - "least squares superposition codes of moderate dictionary size reliable at rates up to capacity"
    - "toward fast reliable communication at rates near capacity with gaussian noise"

Connected through hyperedge HE-4 (induced by source paper "lossy compression via sparse linear regression performance under minimum distance encoding"):
  with members:
    - "lossy compression via sparse linear regression computationally efficient encoding and decoding"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"
    - "least squares superposition codes of moderate dictionary size reliable at rates up to capacity"
    - "toward fast reliable communication at rates near capacity with gaussian noise"
    - "polar codes are optimal for lossy source coding"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "shannon theoretic limits on noisy compressive sampling"

Connected through hyperedge HE-5 (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"
    - "lossy compression of discrete sources via the viterbi algorithm"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "recovery from linear measurements with complexity matching universal signal estimation"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "empirical bayes and full bayes for signal estimation"
    - "compressed sensing via universal denoising and approximate message passing"
    - "performance regions in compressed sensing from noisy measurements"
    - "expectation maximization gaussian mixture approximate message passing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "an mcmc approach to universal lossy compression of analog sources"
    - "an mdl framework for sparse coding and dictionary learning"

Connected through hyperedge HE-7 (induced by source paper "minimum complexity pursuit for universal compressed sensing"):
  with members:
    - "an mcmc approach to universal lossy compression of analog sources"
    - "signal recovery on incoherent manifolds"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "sampling and recovery of pulse streams"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "compressed sensing over ell_p balls minimax mean square error"
    - "model based compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "an mcmc approach to universal lossy compression of analog sources"):
  with members:
    - "on the rate distortion function of bernoulli gaussian sequences"
    - "lossy compression in near linear time via efficient random codebooks and databases"
    - "performance of polar codes for channel and source coding"

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)