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: "optimum trade offs between the error exponent and the excess rate exponent of variable rate slepian wolf coding"
  Abstract: We analyze the optimal trade-off between the error exponent and the excess-rate exponent for variable-rate Slepian-Wolf codes. In particular, we first derive upper (converse) bounds on the optimal error and excess-rate exponents, and then lower (achievable) bounds, via a simple class of variable-rate codes which assign the same rate to all source blocks of the same type class. Then, using the exponent bounds, we derive bounds on the optimal rate functions, namely, the minimal rate assigned to each type class, needed in order to achieve a given target error exponent. The resulting excess-rate exponent is then evaluated. Iterative algorithms are provided for the computation of both bounds on the optimal rate functions and their excess-rate exponents. The resulting Slepian-Wolf codes bridge between the two extremes of fixed-rate coding, which has minimal error exponent and maximal excess-rate exponent, and average-rate coding, which has maximal error exponent and minimal excess-rate exponent.

Connected through hyperedge HE-1 (induced by source paper "on the reliability function of distributed hypothesis testing under optimal detection"):
  with members:
    - "on hypothesis testing against independence with multiple decision centers"
    - "on binary distributed hypothesis testing"
    - "distributed hypothesis testing over noisy channels"
    - "hypothesis testing over the two hop relay network"
    - "a large deviations approach to secure lossy compression"
    - "distributed binary detection with lossy data compression"
    - "neyman pearson test for zero rate multiterminal hypothesis testing"
    - "collaborative distributed hypothesis testing"

Connected through hyperedge HE-2 (induced by source paper "a large deviations approach to secure lossy compression"):
  with members:
    - "measuring secrecy by the probability of a successful guess"
    - "the henchman problem measuring secrecy by the minimum distortion in a list"
    - "rate distortion theory for secrecy systems"
    - "errors and erasures decoding for block codes with feedback"
    - "secrecy is cheap if the adversary must reconstruct"
    - "using a secret key to foil an eavesdropper"
    - "secure lossless compression with side information"
    - "lossless compression with security constraints"

Connected through hyperedge HE-3 (induced by source paper "channel detection in coded communication"):
  with members:
    - "quickest transient change detection under a sampling constraint"
    - "sampling constrained asynchronous communication how to sleep efficiently"
    - "on zero rate error exponents of finite state channels with input dependent states"
    - "simplified erasure list decoding"
    - "list decoding random coding exponents and expurgated exponents"
    - "asymptotically optimal decision rules for joint detection and source coding"
    - "exact random coding error exponents of optimal bin index decoding"
    - "erasure list random coding error exponents are not universally achievable"

Connected through hyperedge HE-4 (induced by source paper "an information spectrum approach to weak variable length source coding with side information"):
  with members:
    - "on the reliability function of variable rate slepian wolf coding"
    - "on the dispersions of three network information theory problems"
    - "variable length compression allowing errors extended"
    - "variable length compression allowing errors"
    - "universal wyner ziv coding for distortion constrained general side information"
    - "second order slepian wolf coding theorems for non mixed and mixed sources"
    - "second order asymptotics in fixed length source coding and intrinsic randomness"

Connected through hyperedge HE-5 (induced by source paper "statistical physics of random binning"):
  with members:
    - "exact random coding error exponents of optimal bin index decoding"
    - "erasure list exponents for slepian wolf decoding"
    - "improved source coding exponents via witsenhausen s rate"
    - "relations between random coding exponents and the statistical physics of random 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)