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: "polar codes are optimal for lossy source coding"
  Abstract: We consider lossy source compression of a binary symmetric source using polar codes and the low-complexity successive encoding algorithm. It was recently shown by Arikan that polar codes achieve the capacity of arbitrary symmetric binary-input discrete memoryless channels under a successive decoding strategy. We show the equivalent result for lossy source compression, i.e., we show that this combination achieves the rate-distortion bound for a binary symmetric source. We further show the optimality of polar codes for various problems including the binary Wyner-Ziv and the binary Gelfand-Pinsker problem

Connected through hyperedge HE-1 (induced by source paper "polar codes and their quantum domain counterparts"):
  with members:
    - "reinforcement learning for nested polar code construction"
    - "decoder tailored polar code design using the genetic algorithm"
    - "genetic algorithm based polar code construction for the awgn channel"
    - "improved bit flipping algorithm for successive cancellation decoding of polar codes"
    - "design of polar codes in 5g new radio"
    - "belief propagation decoding of polar codes on permuted factor graphs"
    - "improved successive cancellation flip decoding of polar codes based on error distribution"
    - "belief propagation list decoding of polar codes"

Connected through hyperedge HE-2 (induced by source paper "list decoding of universal polar codes"):
  with members:
    - "polar codes for the deletion channel weak and strong polarization"
    - "universal polarization for processes with memory"
    - "performance bounds of concatenated polar coding schemes"
    - "soft decision decoding of reed muller codes recursive lists"
    - "fast polarization for processes with memory"
    - "polar coding for processes with memory"
    - "joint successive cancellation decoding of polar codes over intersymbol interference channels"
    - "polar codes with dynamic frozen symbols and their decoding by directed search"

Connected through hyperedge HE-3 (induced by source paper "polar codes simplicity random codes durability"):
  with members:
    - "reed muller codes polarize"
    - "ar i kan meets shannon polar codes with near optimal convergence to channel capacity"
    - "explicit polar codes with small scaling exponent"
    - "on the capacity of channels with deletions and states"
    - "polar codes for the deletion channel weak and strong polarization"
    - "polar like codes and asymptotic tradeoff among block length code rate and error probability"
    - "scaling exponent and moderate deviations asymptotics of polar codes for the awgn channel"
    - "relaxed polar codes"

Connected through hyperedge HE-4 (induced by source paper "sparse regression codes"):
  with members:
    - "the error probability of sparse superposition codes with approximate message passing decoding"
    - "sparcs for unsourced random access"
    - "spatially coupled sparse regression codes design and state evolution analysis"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "the rate distortion function and excess distortion exponent of sparse regression codes with optimal encoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning for nested polar code construction"):
  with members:
    - "decoder in the loop genetic optimization based ldpc code design"
    - "decoder tailored polar code design using the genetic algorithm"
    - "ai coding learning to construct error correction codes"
    - "reinforcement learning from imperfect demonstrations"
    - "deepcode feedback codes via deep learning"
    - "pretraining deep actor critic reinforcement learning algorithms with expert demonstrations"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "sign bit shaping using polar codes"):
  with members:
    - "probabilistically shaped multi level coding with polar codes for fading channels"
    - "code constructions for physical unclonable functions and biometric secrecy systems"
    - "efficient polar code construction for higher order modulation"
    - "polar codes for broadcast channels"
    - "llr based successive cancellation list decoding of polar codes"
    - "how to achieve the capacity of asymmetric channels"
    - "achievable rates for shaped bit metric decoding"
    - "list decoding of polar codes"

Connected through hyperedge HE-7 (induced by source paper "polar coding for common message only wiretap broadcast channel"):
  with members:
    - "achieving secrecy capacity of the wiretap channel and broadcast channel with a confidential component"
    - "polar coding for the broadcast channel with confidential messages a random binning analogy"
    - "efficient one way secret key agreement and private channel coding via polarization"
    - "universal polar codes"
    - "achieving the secrecy capacity of wiretap channels using polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "on the role of mmse estimation in approaching the information theoretic limits of linear gaussian channels shannon meets wiener"

Connected through hyperedge HE-8 (induced by source paper "on polar coding for binary dirty paper"):
  with members:
    - "finite length scaling for polar codes"
    - "improved bounds on the finite length scaling of polar codes"
    - "universal polar codes"
    - "list decoding of polar codes"
    - "polar write once memory codes"
    - "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)