human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "from compression to compressed sensing"):
  Abstract: Can compression algorithms be employed for recovering signals from their underdetermined set of linear measurements? Addressing this question is the first step towards applying compression algorithms for compressed sensing (CS). In this paper, we consider a family of compression algorithms $\mathcal{C}_r$, parametrized by rate $r$, for a compact class of signals $\mathcal{Q} \subset \mathds{R}^n$. The set of natural images and JPEG at different rates are examples of $\mathcal{Q}$ and $\mathcal{C}_r$, respectively. We establish a connection between the rate-distortion performance of $\mathcal{C}_r$, and the number of linear measurements required for successful recovery in CS. We then propose compressible signal pursuit (CSP) algorithm and prove that, with high probability, it accurately and robustly recovers signals from an underdetermined set of linear measurements. We also explore the performance of CSP in the recovery of infinite dimensional signals.

Includes members:
  Member "minimum complexity pursuit for universal compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "solving inverse problems via auto encoders")
  Member "recovery from linear measurements with complexity matching universal signal estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing")
  Member "asymptotic analysis of map estimation via the replica method and applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "lossy compression of discrete sources via the viterbi algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "theoretical links between universal and bayesian compressed sensing algorithms")
  Member "minimum complexity pursuit stability analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "universal compressed sensing")
  Member "information theoretically optimal compressed sensing via spatial coupling and approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "asymptotic analysis of complex lasso via complex approximate message passing camp":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")
  Member "stable restoration and separation of approximately sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "boosting occluded image classification via subspace decomposition based estimation of deep features")

Question: Please tell me which class the source paper 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)