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: "fast polarization for processes with memory"
  Abstract: Fast polarization is crucial for the performance guarantees of polar codes. In the memoryless setting, the rate of polarization is known to be exponential in the square root of the block length. A complete characterization of the rate of polarization for models with memory has been missing. We consider polar codes for processes with memory that are characterized by an underlying ergodic finite state Markov chain. We show that the rate of polarization for these processes is the same as in the memoryless setting, both to the high and to the low entropy sets. Thus, polar codes achieve the Markov capacity in many infromation-theoretic applications.

Connected through hyperedge HE-1 (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"
    - "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"
    - "universal polar codes"

Connected through hyperedge HE-2 (induced by source paper "polar codes for the deletion channel weak and strong polarization"):
  with members:
    - "universal polarization for processes with memory"
    - "capacity upper bounds for deletion type channels"
    - "a simple proof of fast polarization"
    - "polar coding for the binary erasure channel with deletions"
    - "polar coding for processes with memory"
    - "joint successive cancellation decoding of polar codes over intersymbol interference channels"
    - "list decoding of polar codes"
    - "polar codes characterization of exponent bounds and constructions"

Connected through hyperedge HE-3 (induced by source paper "on the capacity of channels with deletions and states"):
  with members:
    - "polar codes for the deletion channel weak and strong polarization"

Connected through hyperedge HE-4 (induced by source paper "universal polarization for processes with memory"):
  with members:
    - "a simple proof of fast polarization"
    - "polar coding for processes with memory"
    - "construction of polar codes for arbitrary discrete memoryless channels"
    - "on the construction of polar codes for channels with moderate input alphabet sizes"
    - "universal polar codes for more capable and less noisy channels and sources"
    - "joint successive cancellation decoding of polar codes over intersymbol interference channels"
    - "universal source polarization and an application to a multi user problem"
    - "channel upgradation for non binary input alphabets and macs"

Connected through hyperedge HE-5 (induced by source paper "a lower bound on the probability of error of polar codes over bms channels"):
  with members:
    - "polar coding for processes with memory"
    - "on the correlation between polarized becs"
    - "list decoding of polar codes"
    - "how to construct polar codes"
    - "performance and construction of polar codes on symmetric binary input memoryless channels"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-6 (induced by source paper "polar coding for processes with memory"):
  with members:
    - "a simple proof of fast polarization"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "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)