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 "linear programming based converses for finite blocklength lossy joint source channel coding"):
  Abstract: A linear programming (LP) based framework is presented for obtaining converses for finite blocklength lossy joint source-channel coding problems. The framework applies for any loss criterion, generalizes certain previously known converses, and also extends to multi-terminal settings. The finite blocklength problem is posed equivalently as a nonconvex optimization problem and using a lift-and-project-like method, a close but tractable LP relaxation of this problem is derived. Lower bounds on the original problem are obtained by the construction of feasible points for the dual of the LP relaxation. A particular application of this approach leads to new converses which recover and improve on the converses of Kostina and Verd ´ u for finite blocklength lossy joint source-channel coding and lossy source coding. For finite blocklength channel coding, the LP relaxation recovers the converse of Polyanskiy, Poor and Verd ´ u and leads to a new improvement on the converse of Wolfowitz, showing thereby that our LP relaxation is asymptotically tight with increasing blocklengths for channel coding, lossless source coding and joint source-channel coding with the excess distortion probability as the loss criterion. Using a duality based argument, a new converse is derived for finite blocklength joint source- channel coding for a class of source-channel pairs. Employing this converse, the LP relaxation is also shown to be tight for all blocklengths for the minimization of the expected average symbol-wise Hamming distortion of a q-ary uniform source over a q-ary symmetric memoryless channel for any q 2 N. The optimization formulation and the lift-and-project method are extended to networked settings and demonstrated by obtaining an improvement on a converse of Zhou et al. for the successive refinement problem for successively refinable source-distortion measure triplets.

Includes members:
  Member "channels with cost constraints strong converse and dispersion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finite sample analysis of image registration")
  Member "generalized sphere packing bound":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "covering codes for insertions and deletions")
  Member "upper bounds on the size of grain correcting codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized sphere packing bound")
  Member "correcting grain errors in magnetic media":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalized sphere packing bound")
  Member "non asymptotic upper bounds for deletion correcting codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "duplication correcting codes")
  Member "lossy joint source channel coding in the finite blocklength regime":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finite sample analysis of image registration")
  Member "fixed length lossy compression in the finite blocklength regime":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finite sample analysis of image registration")

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)