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: "de randomizing shannon the design and analysis of a capacity achieving rateless code"
  Abstract: This paper presents an analysis of spinal codes, a class of rateless codes proposed recently. We prove that spinal codes achieve Shannon capacity for the binary symmetric channel (BSC) and the additive white Gaussian noise (AWGN) channel with an efficient polynomial-time encoder and decoder. They are the first rateless codes with proofs of these properties for BSC and AWGN. The key idea in the spinal code is the sequential application of a hash function over the message bits. The sequential structure of the code turns out to be crucial for efficient decoding. Moreover, counter to the wisdom of having an expander structure in good codes, we show that the spinal code, despite its sequential structure, achieves capacity. The pseudo-randomness provided by a hash function suffices for this purpose. Our proof introduces a variant of Gallager's result characterizing the error exponent of random codes for any memoryless channel. We present a novel application of these error-exponent results within the framework of an efficient sequential code. The application of a hash function over the message bits provides a methodical and effective way to de-randomize Shannon's random codebook construction.

Connected through hyperedge HE-1 (induced by source paper "expansion coding for channel and source coding"):
  with members:
    - "lossy compression of exponential and laplacian sources using expansion coding"
    - "polar codes for the two user multiple access channel"
    - "an mcmc approach to universal lossy compression of analog sources"
    - "polar codes are optimal for lossy source coding"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "channel coding the road to channel capacity"

Connected through hyperedge HE-2 (induced by source paper "scheduling of multicast and unicast services under limited feedback by using rateless codes"):
  with members:
    - "network control without csi using rateless codes for downlink cellular systems"
    - "multiuser scheduling in a markov modeled downlink using randomly delayed arq feedback"
    - "throughput of rateless codes over broadcast erasure channels"
    - "asymptotically optimal downlink scheduling over markovian fading channels"
    - "throughput delay analysis of random linear network coding for wireless broadcasting"
    - "zero rate feedback can achieve the empirical capacity"

Connected through hyperedge HE-3 (induced by source paper "network control without csi using rateless codes for downlink cellular systems"):
  with members:
    - "joint scheduling and resource allocation in ofdma downlink systems via ack nak feedback"
    - "asymptotically optimal downlink scheduling over markovian fading channels"
    - "joint scheduling and arq for mu mimo downlink in the presence of inter cell interference"
    - "rateless coding for gaussian channels"

Connected through hyperedge HE-4 (induced by source paper "deterministic rateless codes for bsc"):
  with members:
    - "rateless coding for gaussian channels"

Connected through hyperedge HE-5 (induced by source paper "near capacity adaptive analog fountain codes for wireless channels"):
  with members:
    - "bayesian compressive sensing via belief propagation"
    - "rateless coding for gaussian channels"

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)