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: "a typical reconstruction limit of compressed sensing based on lp norm minimization"
  Abstract: We consider the problem of reconstructing an $N$-dimensional continuous vector $\bx$ from $P$ constraints which are generated by its linear transformation under the assumption that the number of non-zero elements of $\bx$ is typically limited to $\rho N$ ($0\le \rho \le 1$). Problems of this type can be solved by minimizing a cost function with respect to the $L_p$-norm $||\bx||_p=\lim_{\epsilon \to +0}\sum_{i=1}^N |x_i|^{p+\epsilon}$, subject to the constraints under an appropriate condition. For several $p$, we assess a typical case limit $\alpha_c(\rho)$, which represents a critical relation between $\alpha=P/N$ and $\rho$ for successfully reconstructing the original vector by minimization for typical situations in the limit $N,P \to \infty$ with keeping $\alpha$ finite, utilizing the replica method. For $p=1$, $\alpha_c(\rho)$ is considerably smaller than its worst case counterpart, which has been rigorously derived by existing literature of information theory.

Connected through hyperedge HE-1 (induced by source paper "understanding overfitting peaks in generalization error analytical risk curves for l 2 and l 1 penalized interpolation"):
  with members:
    - "understanding deep learning requires rethinking generalization"
    - "critical behavior and universality classes for an algorithmic phase transition in sparse reconstruction"
    - "the cavity method for analysis of large scale penalized regression"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "mutual information and optimality of approximate message passing in random linear estimation"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "the mutual information in random linear estimation"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"

Connected through hyperedge HE-3 (induced by source paper "scaling limit exact and tractable analysis of online learning algorithms with applications to regularized regression and pca"):
  with members:
    - "hybrid approximate message passing"
    - "the scaling limit of high dimensional online independent component analysis"
    - "online learning for sparse pca in high dimensions exact dynamics and phase transitions"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "sharp time data tradeoffs for linear inverse problems"
    - "living on the edge phase transitions in convex programs with random data"
    - "expectation maximization gaussian mixture approximate message passing"
    - "sparse phase retrieval convex algorithms and limitations"

Connected through hyperedge HE-4 (induced by source paper "fundamental limits of phasemax for phase retrieval a replica analysis"):
  with members:
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "corruption robust phase retrieval via linear programming"
    - "phasemax convex phase retrieval via basis pursuit"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "a field guide to forward backward splitting with a fasta implementation"
    - "sparse phase retrieval convex algorithms and limitations"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "replica symmetry breaking in compressive sensing"):
  with members:
    - "rsb decoupling property of map estimators"
    - "lse precoders for massive mimo with hardware constraints fundamental limits"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "statistical mechanics of map estimation general replica ansatz"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "optimal phase transitions in compressed sensing"
    - "vector precoding for wireless mimo systems and its replica analysis"

Connected through hyperedge HE-6 (induced by source paper "sparse vector recovery bernoulli gaussian message passing"):
  with members:
    - "a lse and sparse message passing based channel estimation for mmwave mimo systems"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "on sparse vector recovery performance in structurally orthogonal matrices via lasso"
    - "expectation maximization gaussian mixture approximate message passing"
    - "support recovery with sparsely sampled free random matrices"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-7 (induced by source paper "statistical mechanics of map estimation general replica ansatz"):
  with members:
    - "glse precoders for massive mimo systems analysis and applications"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "asymptotics of nonlinear lse precoders with applications to transmit antenna selection"
    - "replica symmetry breaking in compressive sensing"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"

Connected through hyperedge HE-8 (induced by source paper "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"):
  with members:
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "s amp approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "on sparse vector recovery performance in structurally orthogonal matrices via lasso"
    - "asymptotic mmse analysis under sparse representation modeling"
    - "statistical mechanics approach to sparse noise denoising"

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)