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: "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
  Abstract: We study the compressed sensing reconstruction problem for a broad class of random, band-diagonal sensing matrices. This construction is inspired by the idea of spatial coupling in coding theory. As demonstrated heuristically and numerically by Krzakala et al. \cite{KrzakalaEtAl}, message passing algorithms can effectively solve the reconstruction problem for spatially coupled measurements with undersampling rates close to the fraction of non-zero coordinates. #R##N#We use an approximate message passing (AMP) algorithm and analyze it through the state evolution method. We give a rigorous proof that this approach is successful as soon as the undersampling rate $\delta$ exceeds the (upper) R\'enyi information dimension of the signal, $\uRenyi(p_X)$. More precisely, for a sequence of signals of diverging dimension $n$ whose empirical distribution converges to $p_X$, reconstruction is with high probability successful from $\uRenyi(p_X)\, n+o(n)$ measurements taken according to a band diagonal matrix. #R##N#For sparse signals, i.e., sequences of dimension $n$ and $k(n)$ non-zero entries, this implies reconstruction from $k(n)+o(n)$ measurements. For `discrete' signals, i.e., signals whose coordinates take a fixed finite set of values, this implies reconstruction from $o(n)$ measurements. The result is robust with respect to noise, does not apply uniquely to random signals, but requires the knowledge of the empirical distribution of the signal $p_X$.

Connected through hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding"):
  with members:
    - "sparse regression codes"
    - "the error probability of sparse superposition codes with approximate message passing decoding"
    - "spatially coupled sparse regression codes design and state evolution analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "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"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"

Connected through hyperedge HE-2 (induced by source paper "finite length scaling of spatially coupled ldpc codes under window decoding over the bec"):
  with members:
    - "spatially coupled turbo like codes"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "spatially coupled sparse codes on graphs theory and practice"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "lossy source coding via spatially coupled ldgm ensembles"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "staircase codes fec for 100 gb s otn"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

Connected through hyperedge HE-3 (induced by source paper "on the compressibility of affinely singular random vectors"):
  with members:
    - "on the information dimension of stochastic processes"
    - "how compressible are innovation processes"
    - "rate distortion dimension of stochastic processes"
    - "universal compressed sensing"

Connected through hyperedge HE-4 (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-5 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models"):
  with members:
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "information theoretic thresholds from the cavity method"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "the layered structure of tensor estimation and its mutual information"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "opening the black box of deep neural networks via information"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"

Connected through hyperedge HE-6 (induced by source paper "information theoretic lower bounds for compressive sensing with generative models"):
  with members:
    - "an introductory guide to fano s inequality with applications in statistical estimation"
    - "lower bounds for compressed sensing with generative models"
    - "solving inverse problems via auto encoders"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "the universal approximation power of finite width deep relu networks"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "benefits of depth in neural networks"
    - "understanding deep neural networks with rectified linear units"

Connected through hyperedge HE-7 (induced by source paper "finite sample analysis of approximate message passing algorithms"):
  with members:
    - "analysis of approximate message passing with a class of non separable denoisers"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "information theoretically optimal sparse pca"

Connected through hyperedge HE-8 (induced by source paper "probabilistic mimo symbol detection with expectation consistency approximate inference"):
  with members:
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "capacity achieving iterative lmmse detection for mimo noma systems"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "spatially coupled ldpc codes constructed from protographs"
    - "optimality of large mimo detection via approximate message passing"
    - "fifty years of mimo detection the road to large scale mimos"
    - "spatially coupled sparse codes on graphs theory and practice"
    - "channel hardening exploiting message passing chemp receiver in large scale mimo systems"

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)