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: "zero delay sequential transmission of markov sources over burst erasure channels"
  Abstract: A setup involving zero-delay sequential transmission of a vector Markov source over a burst erasure channel is studied. A sequence of source vectors is compressed in a causal fashion at the encoder, and the resulting output is transmitted over a burst erasure channel. The destination is required to reconstruct each source vector with zero-delay, but those source sequences that are observed either during the burst erasure, or in the interval of length $W$ following the burst erasure need not be reconstructed. The minimum achievable compression rate is called the rate-recovery function. We assume that each source vector is sampled i.i.d. across the spatial dimension and from a stationary, first-order Markov process across the temporal dimension. #R##N#For discrete sources the case of lossless recovery is considered, and upper and lower bounds on the rate-recovery function are established. Both these bounds can be expressed as the rate for predictive coding, plus a term that decreases at least inversely with the recovery window length $W$. For Gauss-Markov sources and a quadratic distortion measure, upper and lower bounds on the minimum rate are established when $W=0$. These bounds are shown to coincide in the high resolution limit. Finally another setup involving i.i.d. Gaussian sources is studied and the rate-recovery function is completely characterized in this case.

Connected through hyperedge HE-1 (induced by source paper "optimal multiplexed erasure codes for streaming messages with different decoding delays"):
  with members:
    - "optimal streaming codes for channels with burst and arbitrary erasures"
    - "layered constructions for low delay streaming codes"
    - "erasure coding for real time streaming"
    - "streaming codes for channels with burst and isolated erasures"

Connected through hyperedge HE-2 (induced by source paper "capacity of burst noise erasure channels with and without feedback and input cost"):
  with members:
    - "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"
    - "asymptotics of input constrained erasure channel capacity"
    - "feedback does not increase the capacity of compound channels with additive noise"
    - "can feedback increase the capacity of the energy harvesting channel"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "capacity and coding for the ising channel with feedback"
    - "a coding theorem for a class of stationary channels with feedback"
    - "finite state channels with time invariant deterministic feedback"

Connected through hyperedge HE-3 (induced by source paper "optimal zero delay coding of markov sources stationary and finite memory codes"):
  with members:
    - "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"
    - "finite state channels with time invariant deterministic feedback"
    - "the capacity of channels with feedback"

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)