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: "graph memory networks for molecular activity prediction"
  Abstract: Molecular activity prediction is critical in drug design. Machine learning techniques such as kernel methods and random forests have been successful for this task. These models require fixed-size feature vectors as input while the molecules are variable in size and structure. As a result, fixed-sized fingerprint representation is poor in handling substructures for large molecules. Here we approach the problem through deep neural networks as they are flexible in modeling structured data such as grids, sequences and graphs. We propose Graph Memory Network (GraphMem), a memory-augmented neural network to model the graph structure in molecules. GraphMem consists of a recurrent controller coupled with an external memory whose cells dynamically interact and change through a multi-hop reasoning process. The dynamic interactions enable an iterative refinement of the representation of molecular graphs with multiple bond types. We demonstrate the effectiveness of the proposed model on 10 BioAssay activity tests.

Connected through hyperedge HE-1 (induced by source paper "a regularized attention mechanism for graph attention networks"):
  with members:
    - "link prediction based on graph neural networks"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"
    - "convolutional networks on graphs for learning molecular fingerprints"

Connected through hyperedge HE-2 (induced by source paper "memory augmented recursive neural networks"):
  with members:
    - "can neural networks understand logical entailment"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "combining symbolic expressions and black box function evaluations in neural programs"
    - "making neural programming architectures generalize via recursion"
    - "tree memory networks for modelling long term temporal dependencies"
    - "learning continuous semantic representations of symbolic expressions"
    - "learning to transduce with unbounded memory"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-3 (induced by source paper "mol cyclegan a generative model for molecular optimization"):
  with members:
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deep reinforcement learning for de novo drug design"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "junction tree variational autoencoder for molecular graph generation"
    - "syntax directed variational autoencoder for structured data"
    - "molecular de novo design through deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "libmolgrid gpu accelerated molecular gridding for deep learning applications"):
  with members:
    - "potentialnet for molecular property prediction"
    - "optimization of molecules via deep reinforcement learning"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "large scale gan training for high fidelity natural image synthesis"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "atomic convolutional networks for predicting protein ligand binding affinity"
    - "moleculenet a benchmark for molecular machine learning"
    - "generative and discriminative voxel modeling with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "open vocabulary learning on source code with a graph structured cache"):
  with members:
    - "neural program repair by jointly learning to localize and repair"
    - "a general path based representation for predicting program properties"
    - "generative code modeling with graphs"
    - "code completion with neural attention and pointer networks"
    - "relational inductive biases deep learning and graph networks"
    - "tree to tree neural networks for program translation"
    - "object oriented neural programming oonp for document understanding"
    - "neural attribute machines for program generation"

Connected through hyperedge HE-6 (induced by source paper "attentional multilabel learning over graphs a message passing approach"):
  with members:
    - "junction tree variational autoencoder for molecular graph generation"
    - "network representation learning a survey"
    - "order free rnn with visual attention for multi label classification"
    - "representation learning on graphs methods and applications"
    - "graph convolutional networks for classification with a structured label space"
    - "neural message passing for quantum chemistry"
    - "hcp a flexible cnn framework for multi label image classification"
    - "column networks for collective classification"

Connected through hyperedge HE-7 (induced by source paper "graph transformation policy network for chemical reaction prediction"):
  with members:
    - "relational deep reinforcement learning"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "junction tree variational autoencoder for molecular graph generation"
    - "non local neural networks"
    - "attention is all you need"
    - "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-8 (induced by source paper "improving robustness of attention models on graphs"):
  with members:
    - "representation learning on graphs methods and applications"
    - "semi supervised classification with graph convolutional networks"
    - "deep convolutional networks on graph structured data"

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.LG(Machine Learning)