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 "the gaussian rate distortion function of sparse regression codes with optimal encoding"):
  Abstract: We study the rate-distortion performance of Sparse Regression Codes where the codewords are linear combinations of subsets of columns of a design matrix. It is shown that with minimum-distance encoding and squared error distortion, these codes achieve $R^*(D)$, the Shannon rate-distortion function for i.i.d. Gaussian sources. This completes a previous result which showed that $R^*(D)$ was achievable for distortions below a certain threshold. The proof is based on the second moment method, a popular technique to show that a non-negative random variable X is strictly positive with high probability. We first identify the reason behind the failure of the vanilla second moment method for this problem, and then introduce a refinement to show that $R^*(D)$ is achievable for all distortions.

Includes members:
  Member "lossy compression via sparse linear regression computationally efficient encoding and decoding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse regression codes")
  Member "lossy compression via sparse linear regression performance under minimum distance encoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse regression codes")
  Member "chasing the k colorability threshold":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "information theoretic thresholds from the cavity method")
  Member "fast sparse superposition codes have exponentially small error probability for r l c":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "construction of capacity achieving lattice codes polar lattices")
  Member "the dispersion of lossy source coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse regression codes")

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)