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: "new techniques for upper bounding the ml decoding performance of binary linear codes"
  Abstract: In this paper, new techniques are presented to either simplify or improve most existing upper bounds on the maximum-likelihood (ML) decoding performance of the binary linear codes over additive white Gaussian noise (AWGN) channels. Firstly, the recently proposed union bound using truncated weight spectrums by Ma {\em et al} is re-derived in a detailed way based on Gallager's first bounding technique (GFBT), where the "good region" is specified by a sub-optimal list decoding algorithm. The error probability caused by the bad region can be upper-bounded by the tail-probability of a binomial distribution, while the error probability caused by the good region can be upper-bounded by most existing techniques. Secondly, we propose two techniques to tighten the union bound on the error probability caused by the good region. The first technique is based on pair-wise error probabilities, which can be further tightened by employing the independence between the error events and certain components of the received random vectors. The second technique is based on triplet-wise error probabilities, which can be upper-bounded by proving that any three bipolar vectors form a non-obtuse triangle. The proposed bounds improve the conventional union bounds but have a similar complexity since they involve only the $Q$-function. The proposed bounds can also be adapted to bit-error probabilities.

Connected through hyperedge HE-1 (induced by source paper "enumeration of minimum hamming weight polar codewords with sublinear complexity"):
  with members:
    - "fast and flexible successive cancellation list decoders for polar codes"
    - "fast simplified successive cancellation list decoding of polar codes"
    - "llr based successive cancellation list decoding of polar codes"
    - "list decoding of polar codes"
    - "an adaptive successive cancellation list decoder for polar codes with cyclic redundancy check"
    - "on finite length performance of polar codes stopping sets error floor and concatenated design"

Connected through hyperedge HE-2 (induced by source paper "bp led decoding algorithm for ldpc codes over awgn channels"):
  with members:
    - "weight distributions of regular low density parity check codes over finite fields"
    - "tree structure expectation propagation for decoding ldpc codes over binary erasure channels"
    - "lowering the error floor of ldpc codes using cyclic liftings"
    - "tightened upper bounds on the ml decoding error probability of binary linear block codes"

Connected through hyperedge HE-3 (induced by source paper "systematic block markov superposition transmission of repetition codes"):
  with members:
    - "performance analysis of block markov superposition transmission of short codes"
    - "a new class of multiple rate codes based on block markov superposition transmission"
    - "design of spatially coupled ldpc codes over gf q for windowed decoding"
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"
    - "performance comparison of ldpc block and spatially coupled codes over gf q"
    - "block markov superposition transmission construction of big convolutional codes from short codes"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "lossy source coding via spatially coupled ldgm ensembles"

Connected through hyperedge HE-4 (induced by source paper "bounds on the ml decoding error probability of rs coded modulation over awgn channels"):
  with members:
    - "tightened upper bounds on the ml decoding error probability of binary linear block codes"
    - "iterative soft input soft output decoding of reed solomon codes by adapting the parity check matrix"
    - "maximum likelihood decoding of reed solomon codes is np hard"

Connected through hyperedge HE-5 (induced by source paper "block markov superposition transmission construction of big convolutional codes from short codes"):
  with members:
    - "on the minimum distance of generalized spatially coupled ldpc codes"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "serial concatenation of rs codes with kite codes performance analysis iterative decoding and design"
    - "scaling behavior of convolutional ldpc ensembles over the bec"
    - "windowed decoding of spatially coupled codes"
    - "deriving good ldpc convolutional codes from ldpc block codes"

Connected through hyperedge HE-6 (induced by source paper "upper bounds on the ml decoding error probability of general codes over awgn channels"):
  with members:
    - "tightened upper bounds on the ml decoding error probability of binary linear block codes"

Connected through hyperedge HE-7 (induced by source paper "on pseudocodewords and improved union bound of linear programming decoding of hdpc codes"):
  with members:
    - "on pseudocodewords and decision regions of linear programming decoding of hdpc codes"
    - "lower bounds on the minimum pseudodistance for linear codes with q ary psk modulation over awgn"
    - "pseudo codeword analysis of tanner graphs from projective and euclidean planes"
    - "relaxation bounds on the minimum pseudo weight of linear block codes"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc 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)