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: "deriving neural architectures from sequence and graph kernels"
  Abstract: The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process. In this work, we appeal to kernels over combinatorial structures, such as sequences and graphs, to derive appropriate neural operations. We introduce a class of deep recurrent neural operations and formally characterize their associated kernel spaces. Our recurrent modules compare the input to virtual reference objects (cf. filters in CNN) via the kernels. Similar to traditional neural operations, these reference objects are parameterized and directly optimized in end-to-end training. We empirically evaluate the proposed class of neural architectures on standard applications such as language modeling and molecular graph regression, achieving state-of-the-art results across these applications.

Connected through hyperedge HE-1 (induced by source paper "hierarchical generation of molecular graphs using structural motifs"):
  with members:
    - "discrete object generation with reversible inductive construction"
    - "graph u nets"
    - "conditional molecular design with deep generative models"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-2 (induced by source paper "generalization and representational limits of graph neural networks"):
  with members:
    - "directional message passing for molecular graphs"
    - "efficient probabilistic logic reasoning with graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "graph transformer networks"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "on the universality of invariant networks"
    - "approximation ratios of graph neural networks for combinatorial problems"
    - "a survey on graph kernels"

Connected through hyperedge HE-3 (induced by source paper "retrosynthesis prediction with conditional graph logic network"):
  with members:
    - "learning retrosynthetic planning through self play"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "attention is all you need"
    - "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
    - "neural message passing for quantum chemistry"
    - "predicting organic reaction outcomes with weisfeiler lehman network"
    - "gated graph sequence neural networks"
    - "discriminative embeddings of latent variable models for structured data"

Connected through hyperedge HE-4 (induced by source paper "learning representations of graph data a survey"):
  with members:
    - "fast sequence based embedding with diffusion graphs"
    - "adaptive graph convolutional neural networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "inductive representation learning on large graphs"
    - "sparse diffusion convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "3dfacegan adversarial nets for 3d face representation generation and translation"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "unsupervised training for 3d morphable model regression"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "began boundary equilibrium generative adversarial networks"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "progressive growing of gans for improved quality stability and variation"
    - "end to end 3d face reconstruction with deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning universal graph neural network embeddings with aid of transfer learning"):
  with members:
    - "stability and generalization of graph convolutional neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "adaptive graph convolutional neural networks"
    - "the natural language decathlon multitask learning as question answering"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-7 (induced by source paper "provably powerful graph networks"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "towards a practical k dimensional weisfeiler leman algorithm"
    - "on the universality of invariant networks"
    - "a survey on graph kernels"
    - "fast graph representation learning with pytorch geometric"
    - "relational pooling for graph representations"
    - "dual primal graph convolutional networks"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-8 (induced by source paper "learning transferable graph exploration"):
  with members:
    - "synthetic datasets for neural program synthesis"
    - "dom q net grounded rl on structured language"
    - "learning a sat solver from single bit supervision"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "learning to search with mctsnets"
    - "meta reinforcement learning of structured exploration strategies"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "diversity is all you need learning skills without a reward function"

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.NE(Neural and Evolutionary Computing)