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: "physical layer network coding a random coding error exponent perspective"
  Abstract: In this work, we derive the random coding error exponent for the uplink phase of a two-way relay system where physical layer network coding (PNC) is employed. The error exponent is derived for the practical (yet sub-optimum) XOR channel decoding setting. We show that the random coding error exponent under optimum (i.e., maximum likelihood) PNC channel decoding can be achieved even under the sub-optimal XOR channel decoding. The derived achievability bounds provide us with valuable insight and can be used as a benchmark for the performance of practical channel-coded PNC systems employing low complexity decoders when finite-length codewords are used.

Connected through hyperedge HE-1 (induced by source paper "compute and forward relaying with ldpc codes over qpsk scheme"):
  with members:
    - "asymptotic analysis on spatial coupling coding for two way relay channels"
    - "expanding the compute and forward framework unequal powers signal levels and multiple linear combinations"
    - "network coded multiple access"
    - "universal codes for the gaussian mac via spatial coupling"
    - "compute and forward harnessing interference through structured codes"
    - "joint physical layer coding and network coding for bi directional relaying"

Connected through hyperedge HE-2 (induced by source paper "secure modulo sum via multiple access channel"):
  with members:
    - "secure computation and forward with linear codes"
    - "secure physical layer network coding versus secure network coding"
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "on secure computation over the binary modulo 2 adder multiple access wiretap channel"
    - "expanding the compute and forward framework unequal powers signal levels and multiple linear combinations"
    - "secure compute and forward in a bidirectional relay"
    - "adaptive compute and forward with lattice codes over algebraic integers"
    - "lattices over eisenstein integers for compute and forward"

Connected through hyperedge HE-3 (induced by source paper "asymptotic analysis on spatial coupling coding for two way relay channels"):
  with members:
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "channel coding and decoding in a relay system operated with physical layer network coding"
    - "compute and forward harnessing interference through structured codes"
    - "joint physical layer coding and network coding for bi directional relaying"
    - "density evolution for asymmetric memoryless channels"

Connected through hyperedge HE-4 (induced by source paper "secure physical layer network coding versus secure network coding"):
  with members:
    - "secure computation and forward with linear codes"
    - "secure modulo sum via multiple access channel"
    - "expanding the compute and forward framework unequal powers signal levels and multiple linear combinations"
    - "secure compute and forward in a bidirectional relay"
    - "strong secrecy and reliable byzantine detection in the presence of an untrusted relay"
    - "compute and forward harnessing interference through structured codes"

Connected through hyperedge HE-5 (induced by source paper "secure computation and forward with linear codes"):
  with members:
    - "secure compute and forward in a bidirectional relay"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"
    - "strong secrecy and reliable byzantine detection in the presence of an untrusted relay"

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)