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 coding for achieving the capacity of marginal channels in nonbinary input setting"
  Abstract: Achieving information-theoretic security using explicit coding scheme in which unlimited computational power for eavesdropper is assumed, is one of the main topics is security consideration. It is shown that polar codes are capacity achieving codes and have a low complexity in encoding and decoding. It has been proven that polar codes reach to secrecy capacity in the binary-input wiretap channels in symmetric settings for which the wiretapper's channel is degraded with respect to the main channel. The first task of this paper is to propose a coding scheme to achieve secrecy capacity in asymmetric nonbinary-input channels while keeping reliability and security conditions satisfied. Our assumption is that the wiretap channel is stochastically degraded with respect to the main channel and message distribution is unspecified. The main idea is to send information set over good channels for Bob and bad channels for Eve and send random symbols for channels that are good for both. In this scheme the frozen vector is defined over all possible choices using polar codes ensemble concept. We proved that there exists a frozen vector for which the coding scheme satisfies reliability and security conditions. It is further shown that uniform distribution of the message is the necessary condition for achieving secrecy capacity.

Connected through hyperedge HE-1 (induced by source paper "sparse architectures for text independent speaker verification using deep neural networks"):
  with members:
    - "attention based guided structured sparsity of deep neural networks"
    - "speechpy a library for speech processing and recognition"
    - "text independent speaker verification using 3d convolutional neural networks"
    - "voxceleb a large scale speaker identification dataset"
    - "3d convolutional neural networks for cross audio visual matching recognition"
    - "on the construction of polar codes for achieving the capacity of marginal channels"
    - "learning structured sparsity in deep neural networks"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-2 (induced by source paper "on the construction of polar codes for achieving the capacity of marginal channels"):
  with members:
    - "soft decision decoding of reed muller codes recursive lists"
    - "achieving the secrecy capacity of wiretap channels using polar codes"
    - "nested polar codes for wiretap and relay channels"
    - "strong secrecy for erasure wiretap channels"
    - "performance of polar codes for channel and source coding"
    - "polarization for arbitrary discrete memoryless channels"
    - "invertible extractors and wiretap protocols"
    - "on the rate of channel polarization"

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)