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: "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.

Connected through hyperedge HE-1 (induced by source paper "minimax theorems for finite blocklength lossy joint source channel coding over an avc"):
  with members:
    - "finite blocklength and dispersion bounds for the arbitrarily varying channel"
    - "shannon meets von neumann a minimax theorem for channel coding in the presence of a jammer"
    - "improved finite blocklength converses for slepian wolf coding via linear programming"
    - "lossy joint source channel coding in the finite blocklength regime"
    - "fixed length lossy compression in the finite blocklength regime"

Connected through hyperedge HE-2 (induced by source paper "strong converse using change of measure arguments"):
  with members:
    - "a new proof of nonsignalling multiprover parallel repetition theorem"
    - "communication for generating correlation a survey"
    - "communication for generating correlation a unifying survey"
    - "improved finite blocklength converses for slepian wolf coding via linear programming"
    - "second order region for gray wyner network"
    - "strong converses are just edge removal properties"
    - "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"
    - "wiretap channel capacity secrecy criteria strong converse and phase change"

Connected through hyperedge HE-3 (induced by source paper "improved finite blocklength converses for slepian wolf coding via linear programming"):
  with members:
    - "lossy joint source channel coding in the finite blocklength regime"
    - "fixed length lossy compression in the finite blocklength regime"

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)