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: "hyper graph network decoders for block codes"
  Abstract: Neural decoders were shown to outperform classical message passing techniques for short BCH codes. In this work, we extend these results to much larger families of algebraic block codes, by performing message passing with graph neural networks. The parameters of the sub-network at each variable-node in the Tanner graph are obtained from a hypernetwork that receives the absolute values of the current message as input. To add stability, we employ a simplified version of the arctanh activation that is based on a high order Taylor approximation of this activation function. Our results show that for a large number of algebraic block codes, from diverse families of codes (BCH, LDPC, Polar), the decoding obtained with our method outperforms the vanilla belief propagation method as well as other learning techniques from the literature.

Connected through hyperedge HE-1 (induced by source paper "molecule property prediction and classification with graph hypernetworks"):
  with members:
    - "incidence networks for geometric deep learning"
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "relational pooling for graph representations"
    - "deepcode feedback codes via deep learning"
    - "graph hypernetworks for neural architecture search"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-2 (induced by source paper "a gated hypernet decoder for polar codes"):
  with members:
    - "molecule property prediction and classification with graph hypernetworks"
    - "neural belief propagation decoding of crc polar concatenated codes"
    - "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"
    - "on deep learning based channel decoding"
    - "smash one shot model architecture search through hypernetworks"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-3 (induced by source paper "electric analog circuit design with hypernetworks and a differential simulator"):
  with members:
    - "deep meta functionals for shape representation"
    - "csi net unified human body characterization and action recognition"
    - "learning to design circuits"
    - "deep residual learning for image recognition"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)