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: "encoding for the blackwell channel with reinforced belief propagation"
  Abstract: A key idea in coding for the broadcast channel (BC) is binning, in which the transmitter encode information by selecting a codeword from an appropriate bin (the messages are thus the bin indexes). This selection is normally done by solving an appropriate (possibly difficult) combinatorial problem. Recently it has been shown that binning for the Blackwell channel -a particular BC- can be done by iterative schemes based on Survey Propagation (SP). This method uses decimation for SP and suffers a complexity of O(n2). In this paper we propose a new variation of the Belief Propagation (BP) algorithm, named Reinforced BP algorithm, that turns BP into a solver. Our simulations show that this new algorithm has complexity O(n log n). Using this new algorithm together with a non-linear coding scheme, we can efficiently achieve rates close to the border of the capacity region of the Blackwell channel.

Connected through hyperedge HE-1 (induced by source paper "polar codes for broadcast channels"):
  with members:
    - "achieving marton s region for broadcast channels using polar codes"
    - "a comparison of superposition coding schemes"
    - "polar codes for the two user multiple access channel"
    - "evaluation of marton s inner bound for the general broadcast channel"
    - "polar codes for q ary channels q 2 r"
    - "how to construct polar codes"
    - "achieving the secrecy capacity of wiretap channels using polar codes"
    - "multilevel polarization of polar codes over arbitrary discrete memoryless channels"

Connected through hyperedge HE-2 (induced by source paper "structured sublinear compressive sensing via belief propagation"):
  with members:
    - "sparse reconstruction via the reed muller sieve"
    - "multiple bases belief propagation decoding of high density cyclic codes"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "lp decoding meets lp decoding a connection between channel coding and compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "bayesian compressive sensing via belief propagation"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-3 (induced by source paper "efficient ldpc codes over gf q for lossy data compression"):
  with members:
    - "lower bounds on the rate distortion function of ldgm codes"
    - "lower bounds on the rate distortion function of individual ldgm codes"
    - "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"
    - "analysis of ldgm and compound codes for lossy compression and binning"
    - "low density constructions can achieve the wyner ziv and gelfand pinsker bounds"
    - "design and analysis of nonbinary ldpc codes for arbitrary discrete memoryless channels"
    - "statistical mechanical approach to lossy data compression theory and practice"
    - "lossy source encoding via message passing and decimation over generalized codewords of ldgm 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)