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: "extremes of error exponents"
  Abstract: This paper determines the range of feasible values of standard error exponents for binary-input memoryless symmetric channels of fixed capacity $C$ and shows that extremes are attained by the binary symmetric and the binary erasure channel. The proof technique also provides analogous extremes for other quantities related to Gallager's $E_0$ function, such as the cutoff rate, the Bhattacharyya parameter, and the channel dispersion.

Connected through hyperedge HE-1 (induced by source paper "a lower bound on the interactive capacity of binary memoryless symmetric channels"):
  with members:
    - "interactive channel capacity revisited"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-2 (induced by source paper "performance bounds of concatenated polar coding schemes"):
  with members:
    - "parity check polar coding for 5g and beyond"
    - "deep learning methods for improved decoding of linear codes"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "on deep learning based channel decoding"
    - "coding in the finite blocklength regime bounds based on laplace integrals and their asymptotic approximations"
    - "learning to decode linear codes using deep learning"
    - "code design for short blocks a survey"
    - "symbol decision successive cancellation list decoder for polar codes"

Connected through hyperedge HE-3 (induced by source paper "sharp bounds on arimoto s conditional r e nyi entropies between two distinct orders"):
  with members:
    - "encoding tasks and renyi entropy"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"

Connected through hyperedge HE-4 (induced by source paper "re proving channel polarization theorems an extremality and robustness analysis"):
  with members:
    - "universal polar codes for more capable and less noisy channels and sources"
    - "the symmetric convex ordering a novel partial order for b dmcs ordering the information sets of polar codes"
    - "properties of the polarization transformations for the likelihood ratios of symmetric b dmcs"
    - "extremality for gallager s reliability function e_0"
    - "list decoding of polar codes"
    - "on the construction of polar codes"
    - "how to construct polar codes"
    - "hardware architectures for successive cancellation decoding of polar codes"

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)