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: "improved source coding exponents via witsenhausen s rate"
  Abstract: We provide a novel upper-bound on Witsenhausen's rate, the rate required in the zero-error analogue of the Slepian-Wolf problem; our bound is given in terms of a new information-theoretic functional defined on a certain graph. We then use the functional to give a single letter lower-bound on the error exponent for the Slepian-Wolf problem under the vanishing error probability criterion, where the decoder has full (i.e. unencoded) side information. Our exponent stems from our new encoding scheme which makes use of source distribution only through the positions of the zeros in the `channel' matrix connecting the source with the side information, and in this sense is `semi-universal'. We demonstrate that our error exponent can beat the `expurgated' source-coding exponent of Csisz\'{a}r and K\"{o}rner, achievability of which requires the use of a non-universal maximum-likelihood decoder. An extension of our scheme to the lossy case (i.e. Wyner-Ziv) is given. For the case when the side information is a deterministic function of the source, the exponent of our improved scheme agrees with the sphere-packing bound exactly (thus determining the reliability function). An application of our functional to zero-error channel capacity is also given.

Connected through hyperedge HE-1 (induced by source paper "on hypothesis testing against independence with multiple decision centers"):
  with members:
    - "hypothesis testing under maximal leakage privacy constraints"
    - "distributed hypothesis testing over noisy channels"
    - "collaborative distributed hypothesis testing"
    - "on the capacity of the discrete memoryless broadcast channel with feedback"

Connected through hyperedge HE-2 (induced by source paper "statistical physics of random binning"):
  with members:
    - "exact random coding error exponents of optimal bin index decoding"
    - "optimum trade offs between the error exponent and the excess rate exponent of variable rate slepian wolf coding"
    - "erasure list exponents for slepian wolf decoding"
    - "relations between random coding exponents and the statistical physics of random codes"

Connected through hyperedge HE-3 (induced by source paper "optimum trade offs between the error exponent and the excess rate exponent of variable rate slepian wolf coding"):
  with members:
    - "on zero rate error exponents of finite state channels with input dependent states"
    - "erasure list exponents for slepian wolf decoding"

Connected through hyperedge HE-4 (induced by source paper "optimality of binning for distributed hypothesis testing"):
  with members:
    - "reliability in source coding with side information"
    - "the gaussian many help one distributed source coding problem"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-5 (induced by source paper "reliability in source coding with side information"):
  with members:
    - "optimality of binning for distributed hypothesis testing"
    - "capacity and random coding exponents for channel coding with side information"

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)