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: "neural offset min sum decoding"
  Abstract: Recently, it was shown that if multiplicative weights are assigned to the edges of a Tanner graph used in belief propagation decoding, it is possible to use deep learning techniques to find values for the weights which improve the error-correction performance of the decoder. Unfortunately, this approach requires many multiplications, which are generally expensive operations. In this paper, we suggest a more hardware-friendly approach in which offset min-sum decoding is augmented with learnable offset parameters. Our method uses no multiplications and has a parameter count less than half that of the multiplicative algorithm. This both speeds up training and provides a feasible path to hardware architectures. After describing our method, we compare the performance of the two neural decoding algorithms and show that our method achieves error-correction performance within 0.1 dB of the multiplicative approach and as much as 1 dB better than traditional belief propagation for the codes under consideration.

Connected through hyperedge HE-1 (induced by source paper "deep learning for ul dl channel calibration in generic massive mimo systems"):
  with members:
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "achievable rate maximization by passive intelligent mirrors"
    - "asymptotically optimal estimation algorithm for the sparse signal with arbitrary distributions"
    - "a framework for over the air reciprocity calibration for tdd massive mimo systems"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"
    - "reciprocity calibration for massive mimo proposal modeling and validation"
    - "learning to communicate channel auto encoders domain specific regularizers and attention"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "zero shot deep hashing and neural network based error correction for face template protection"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "learning to authenticate with deep multibiometric hashing and neural network decoding"
    - "adversarial examples to fool iris recognition systems"
    - "deep learning methods for improved decoding of linear codes"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "multibiometric secure system based on deep learning"

Connected through hyperedge HE-3 (induced by source paper "learning to authenticate with deep multibiometric hashing and neural network decoding"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "deep learning methods for improved decoding of linear codes"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "multibiometric secure system based on deep learning"
    - "rnn decoding of linear block codes"
    - "learning to decode linear codes using deep learning"
    - "the nd iris 0405 iris image dataset"

Connected through hyperedge HE-4 (induced by source paper "neural dynamic successive cancellation flip decoding of polar codes"):
  with members:
    - "improved bit flipping algorithm for successive cancellation decoding of polar codes"
    - "neural belief propagation decoding of crc polar concatenated codes"
    - "partitioned successive cancellation flip decoding of polar codes"
    - "deep learning methods for improved decoding of linear codes"
    - "improved successive cancellation flip decoding of polar codes based on error distribution"
    - "dynamic scflip decoding of polar codes"
    - "tensorflow a system for large scale machine learning"
    - "llr based successive cancellation list decoding of polar codes"

Connected through hyperedge HE-5 (induced by source paper "hyper graph network decoders for block codes"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "graph hypernetworks for neural architecture search"
    - "deep learning methods for improved decoding of linear codes"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "smash one shot model architecture search through hypernetworks"
    - "learning feed forward one shot learners"
    - "learning to decode linear codes using deep learning"
    - "dynamic filter networks"

Connected through hyperedge HE-6 (induced by source paper "a very brief introduction to machine learning with applications to communication systems"):
  with members:
    - "proactive received power prediction using machine learning and depth images for mmwave networks"
    - "end to end deep learning of optical fiber communications"
    - "deepcode feedback codes via deep learning"
    - "machine learning at the edge a data driven architecture with applications to 5g cellular networks"
    - "online label recovery for deep learning based communication through error correcting codes"
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning in physical layer communications"
    - "deep learning methods for improved decoding of linear codes"

Connected through hyperedge HE-7 (induced by source paper "deep learning methods for improved decoding of linear codes"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "performance bounds of concatenated polar coding schemes"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "rnn decoding of linear block codes"
    - "detection algorithms for communication systems using deep learning"
    - "on deep learning based channel decoding"
    - "learning to decode linear codes using deep learning"
    - "code design for short blocks a survey"

Connected through hyperedge HE-8 (induced by source paper "learning from the syndrome"):
  with members:
    - "online label recovery for deep learning based communication through error correcting codes"
    - "deep learning methods for improved decoding of linear codes"
    - "deep learning for decoding of linear codes a syndrome based approach"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "rnn decoding of linear block codes"
    - "learning to decode linear codes using deep learning"
    - "adam a method for stochastic optimization"
    - "iterative soft input soft output decoding of reed solomon codes by adapting the parity check matrix"

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)