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: "the third order term in the normal approximation for singular channels"
  Abstract: For a singular and symmetric discrete memoryless channel with positive dispersion, the third-order term in the normal approximation is shown to be upper bounded by a constant. This finding completes the characterization of the third-order term for symmetric discrete memoryless channels. The proof method is extended to asymmetric and singular channels with constant composition codes, and its connection to existing results, as well as its limitation in the error exponents regime, are discussed.

Connected through hyperedge HE-1 (induced by source paper "asymptotic estimates in information theory with non vanishing error probabilities"):
  with members:
    - "second order asymptotics for the gaussian mac with degraded message sets"
    - "the third order term in the normal approximation for the awgn channel"
    - "strong converse and second order asymptotics of channel resolvability"
    - "channels with cost constraints strong converse and dispersion"
    - "secret key agreement general capacity and second order asymptotics"
    - "second order coding rates for channels with state"
    - "unequal message protection asymptotic and non asymptotic tradeoffs"
    - "information spectrum approach to strong converse theorems for degraded wiretap channels"

Connected through hyperedge HE-2 (induced by source paper "the third order term in the normal approximation for the awgn channel"):
  with members:
    - "second order asymptotics for the gaussian mac with degraded message sets"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "refinement of the random coding bound"
    - "a derivation of the asymptotic random coding prefactor"
    - "the log volume of optimal codes for memoryless channels asymptotically within a few nats"
    - "a finite blocklength perspective on gaussian multi access channels"
    - "refinement of the sphere packing bound asymmetric channels"

Connected through hyperedge HE-3 (induced by source paper "fixed error asymptotics for erasure and list decoding"):
  with members:
    - "the third order term in the normal approximation for the awgn channel"
    - "list decoding random coding exponents and expurgated exponents"
    - "on the dispersions of three network information theory problems"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "feedback communication systems with limitations on incremental redundancy"
    - "erasure list exponents for slepian wolf decoding"
    - "reliability based error detection for feedback communication with low latency"
    - "the log volume of optimal codes for memoryless channels asymptotically within a few nats"

Connected through hyperedge HE-4 (induced by source paper "unequal message protection asymptotic and non asymptotic tradeoffs"):
  with members:
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "random access and source channel coding error exponents for multiple access channels"
    - "the log volume of optimal codes for memoryless channels asymptotically within a few nats"
    - "the awgn red alert problem"
    - "moderate deviations in channel coding"
    - "lossy joint source channel coding in the finite blocklength regime"
    - "the dispersion of joint source channel coding"
    - "unequal error protection an information theoretic perspective"

Connected through hyperedge HE-5 (induced by source paper "refinement of the random coding bound"):
  with members:
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "a derivation of the asymptotic random coding prefactor"
    - "moderate deviations in channel coding"
    - "refinement of the sphere packing bound asymmetric 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 "the log volume of optimal codes for memoryless channels asymptotically within a few nats"):
  with members:
    - "channels with cost constraints strong converse and dispersion"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "the importance of tie breaking in finite blocklength bounds"
    - "information spectrum approach to second order coding rate in channel coding"

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)