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 lattice decoders"
  Abstract: Lattice decoders constructed with neural networks are presented. Firstly, we show how the fundamental parallelotope is used as a compact set for the approximation by a neural lattice decoder. Secondly, we introduce the notion of Voronoi-reduced lattice basis. As a consequence, a first optimal neural lattice decoder is built from Boolean equations and the facets of the Voronoi cell. This decoder needs no learning. Finally, we present two neural decoders with learning. It is shown that L1 regularization and {\em a priori} information about the lattice structure lead to a simplification of the model.

Connected through hyperedge HE-1 (induced by source paper "a lattice based approach to the expressivity of deep relu neural networks"):
  with members:
    - "on the cvp for the root lattices via folding with deep relu neural networks"
    - "deep learning methods for improved decoding of linear codes"
    - "depth separation for neural networks"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "on deep learning based channel decoding"
    - "benefits of depth in neural networks"
    - "understanding deep neural networks with rectified linear units"
    - "depth width tradeoffs in approximating natural functions with neural networks"

Connected through hyperedge HE-2 (induced by source paper "on the cvp for the root lattices via folding with deep relu neural networks"):
  with members:
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "benefits of depth in neural networks"
    - "understanding deep neural networks with rectified linear units"
    - "depth width tradeoffs in approximating natural functions with neural networks"
    - "the power of depth for feedforward neural networks"
    - "on the number of response regions of deep feed forward networks with piece wise linear activations"

Connected through hyperedge HE-3 (induced by source paper "multilevel mimo detection with deep learning"):
  with members:
    - "learning to detect"
    - "deep learning aided projected gradient detector for massive overloaded mimo channels"
    - "a model driven deep learning network for mimo detection"
    - "learning to decode linear codes using deep learning"
    - "deep unfolding model based inspiration of novel deep architectures"

Connected through hyperedge HE-4 (induced by source paper "a neural network lattice decoding algorithm"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "learning to decode linear codes using deep learning"

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)