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: "randomness and dependencies extraction via polarization with applications to slepian wolf coding and secrecy"
  Abstract: The polarization phenomenon for a single source is extended to a framework with multiple correlated sources. It is shown in addition to extracting the randomness of the source, the polar transforms takes the original arbitrary dependencies to extremal dependencies. Polar coding schemes for the Slepian-Wolf problem and for secret key generations are then proposed based on this phenomenon. In particular, constructions of secret keys achieving the secrecy capacity and compression schemes achieving the Slepian-Wolf capacity region are obtained with a complexity of $O(n \log (n))$.

Connected through hyperedge HE-1 (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-2 (induced by source paper "entropic matroids and their representation"):
  with members:
    - "polar codes for the two user multiple access channel"
    - "mutual information matroids and extremal dependencies"
    - "polar codes for the m user mac"
    - "existence of new inequalities for representable polymatroids"

Connected through hyperedge HE-3 (induced by source paper "achieving the uniform rate region of general multiple access channels by polar coding"):
  with members:
    - "achieving marton s region for broadcast channels using polar codes"
    - "performance limits and practical decoding of interleaved reed solomon polar concatenated codes"
    - "polar coding for interference networks"
    - "compound polar codes"
    - "achieving the uniform rate region of multiple access channels using polar codes"
    - "list decoding of polar codes"
    - "polar codes for the two user multiple access channel"
    - "achieving the secrecy capacity of wiretap channels using polar codes"

Connected through hyperedge HE-4 (induced by source paper "how to achieve the capacity of asymmetric channels"):
  with members:
    - "on the scaling exponent of polar codes for binary input energy harvesting channels"
    - "threshold saturation for nonbinary sc ldpc codes on the binary erasure channel"
    - "construction of polar codes with sublinear complexity"
    - "polar codes for broadcast channels"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "finite length scaling for polar codes"
    - "achieving marton s region for broadcast channels using polar codes"

Connected through hyperedge HE-5 (induced by source paper "reed muller codes for random erasures and errors"):
  with members:
    - "recursive decoding and its performance for low rate reed muller codes"
    - "from polar to reed muller codes a technique to improve the finite length performance"
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "smoothed analysis of tensor decompositions"
    - "efficient density estimation via piecewise polynomial approximation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "channel coding the road to channel capacity"

Connected through hyperedge HE-6 (induced by source paper "polar coding for secret key generation"):
  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 and constrained randomization"
    - "universal source polarization and an application to a multi user problem"
    - "efficient one way secret key agreement and private channel coding via polarization"
    - "secure biometrics concepts authentication architectures and challenges"
    - "separation of reliability and secrecy in rate limited secret key generation"
    - "strong coordination with polar codes"
    - "achieving the secrecy capacity of wiretap channels using polar codes"

Connected through hyperedge HE-7 (induced by source paper "how to construct polar codes"):
  with members:
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "universal bounds on the scaling behavior of polar codes"
    - "list decoding of polar codes"
    - "polar codes for the two user multiple access channel"
    - "on the construction of polar codes"
    - "polar write once memory codes"
    - "achieving the secrecy capacity of wiretap channels using polar codes"
    - "properties and construction 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)