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 "information theoretic lower bounds for compressive sensing with generative models"):
  Abstract: The goal of standard compressive sensing is to estimate an unknown vector from linear measurements under the assumption of sparsity in some basis. Recently, it has been shown that significantly fewer measurements may be required if the sparsity assumption is replaced by the assumption that the unknown vector lies near the range of a suitably-chosen generative model. In particular, in (Bora {\em et al.}, 2017) it was shown that roughly $O(k\log L)$ random Gaussian measurements suffice for accurate recovery when the $k$-input generative model is bounded and $L$-Lipschitz, and that $O(kd \log w)$ measurements suffice for $k$-input ReLU networks with depth $d$ and width $w$. In this paper, we establish corresponding algorithm-independent lower bounds on the sample complexity using tools from minimax statistical analysis. In accordance with the above upper bounds, our results are summarized as follows: (i) We construct an $L$-Lipschitz generative model capable of generating group-sparse signals, and show that the resulting necessary number of measurements is $\Omega(k \log L)$; (ii) Using similar ideas, we construct two-layer ReLU networks of high width requiring $\Omega(k \log w)$ measurements, as well as lower-width deep ReLU networks requiring $\Omega(k d)$ measurements. As a result, we establish that the scaling laws derived in (Bora {\em et al.}, 2017) are optimal or near-optimal in the absence of further assumptions.

Includes members:
  Member "an introductory guide to fano s inequality with applications in statistical estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning adversarially robust representations via worst case mutual information maximization")
  Member "lower bounds for compressed sensing with generative models":
    also connected through other hyperedges:
      (none)
  Member "solving inverse problems via auto encoders":
    also connected through other hyperedges:
      (none)
  Member "deep decoder concise image representations from untrained non convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "image recognition from raw labels collected without annotators")
  Member "the universal approximation power of finite width deep relu networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the oracle of dlphi")
  Member "global guarantees for enforcing deep generative priors by empirical risk":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "benefits of depth in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lossless compression of deep neural networks")
  Member "understanding deep neural networks with rectified linear units":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")

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)