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: "the stochastic interpolation method a simple scheme to prove replica formulas in bayesian inference"
  Abstract: In recent years important progress has been achieved towards proving the validity of the replica predictions for the (asymptotic) mutual information (or "free energy") in Bayesian inference problems. The proof techniques that have emerged appear to be quite general, despite they have been worked out on a case-by-case basis. Unfortunately, a common point between all these schemes is their relatively high level of technicality. We present a new proof scheme that is quite straightforward with respect to the previous ones. We call it the adaptive interpolation method because it can be seen as an extension of the interpolation method developped by Guerra and Toninelli in the context of spin glasses, with an interpolation path that is adaptive. In order to illustrate our method we show how to prove the replica formula for three non-trivial inference problems. The first one is symmetric rank-one matrix estimation (or factorisation), which is the simplest problem considered here and the one for which the method is presented in full details. Then we generalize to symmetric tensor estimation and random linear estimation. We believe that the present method has a much wider range of applicability and also sheds new insights on the reasons for the validity of replica formulas in Bayesian inference.

Connected through hyperedge HE-1 (induced by source paper "entropy and mutual information in models of deep neural networks"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "the layered structure of tensor estimation and its mutual information"
    - "multi layer generalized linear estimation"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "opening the black box of deep neural networks via information"
    - "inference in deep networks in high dimensions"
    - "estimating mixture entropy with pairwise distances"
    - "the mutual information in random linear estimation"

Connected through hyperedge HE-2 (induced by source paper "glse precoders for massive mimo systems analysis and applications"):
  with members:
    - "stepwise transmit antenna selection in downlink massive multiuser mimo"
    - "nonlinear precoders for massive mimo systems with general constraints"
    - "asymptotics of nonlinear lse precoders with applications to transmit antenna selection"
    - "asymptotics of transmit antenna selection impact of multiple receive antennas"
    - "energy efficient coordinated multi cell multi group multicast beamforming with antenna selection"
    - "a new class of nonlinear precoders for hardware efficient massive mimo systems"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "rsb decoupling property of map estimators"

Connected through hyperedge HE-3 (induced by source paper "the mutual information in random linear estimation beyond i i d matrices"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "the layered structure of tensor estimation and its mutual information"
    - "multi layer generalized linear estimation"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "hybrid approximate message passing"
    - "phase transitions optimal errors and optimality of message passing in generalized linear models"
    - "mutual information and optimality of approximate message passing in random linear estimation"

Connected through hyperedge HE-4 (induced by source paper "marvels and pitfalls of the langevin algorithm in noisy high dimensional inference"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "information theoretic thresholds from the cavity method"
    - "bayesian estimation from few samples community detection and related problems"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "information theoretically optimal sparse pca"
    - "a statistical model for tensor pca"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-5 (induced by source paper "estimation in the spiked wigner model a short proof of the replica formula"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "the layered structure of tensor estimation and its mutual information"
    - "phase transitions optimal errors and optimality of message passing in generalized linear models"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "phase transitions in sparse pca"
    - "a statistical model for tensor pca"

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)