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: "new approach to bayesian high dimensional linear regression"
  Abstract: Consider the problem of estimating parameters $X^n \in \mathbb{R}^n $, generated by a stationary process, from $m$ response variables $Y^m = AX^n+Z^m$, under the assumption that the distribution of $X^n$ is known. This is the most general version of the Bayesian linear regression problem. The lack of computationally feasible algorithms that can employ generic prior distributions and provide a good estimate of $X^n$ has limited the set of distributions researchers use to model the data. In this paper, a new scheme called Q-MAP is proposed. The new method has the following properties: (i) It has similarities to the popular MAP estimation under the noiseless setting. (ii) In the noiseless setting, it achieves the "asymptotically optimal performance" when $X^n$ has independent and identically distributed components. (iii) It scales favorably with the dimensions of the problem and therefore is applicable to high-dimensional setups. (iv) The solution of the Q-MAP optimization can be found via a proposed iterative algorithm which is provably robust to the error (noise) in the response variables.

Connected through hyperedge HE-1 (induced by source paper "solving inverse problems via auto encoders"):
  with members:
    - "amp inspired deep networks for sparse linear inverse problems"
    - "deep convolutional neural network for inverse problems in imaging"
    - "deep fully connected networks for video compressive sensing"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "u net convolutional networks for biomedical image segmentation"
    - "a deep learning approach to structured signal recovery"
    - "deep learning face attributes in the wild"
    - "from compression to compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "denoising of structured random processes"):
  with members:
    - "deep image prior"

Connected through hyperedge HE-3 (induced by source paper "theoretical links between universal and bayesian compressed sensing algorithms"):
  with members:
    - "an efficient algorithm for compression based compressed sensing"
    - "on the information dimension of stochastic processes"
    - "compression based compressed sensing"
    - "from denoising to compressed sensing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "lossy compression of discrete sources via the viterbi algorithm"

Connected through hyperedge HE-4 (induced by source paper "an efficient algorithm for compression based compressed sensing"):
  with members:
    - "compression based compressed sensing"
    - "from denoising to compressed sensing"
    - "from compression to compressed sensing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "model based compressive sensing"
    - "sampling and reconstructing signals from a union of linear subspaces"
    - "iterative hard thresholding for compressed sensing"

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)