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 graph machines learning neural networks using graphs"
  Abstract: Label propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural network architectures, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a training objective for neural networks, Neural Graph Machines, for combining the power of neural networks and label propagation. The new objective allows the neural networks to harness both labeled and unlabeled data by: (a) allowing the network to train using labeled data as in the supervised setting, (b) biasing the network to learn similar hidden representations for neighboring nodes on a graph, in the same vein as label propagation. Such architectures with the proposed objective can be trained efficiently using stochastic gradient descent and scaled to large graphs. The proposed method is experimentally validated on a wide range of tasks (multi- label classification on social graphs, news categorization and semantic intent classification) using different architectures (NNs, CNNs, and LSTM RNNs).

Connected through hyperedge HE-1 (induced by source paper "efficient continual learning in neural networks with embedding regularization"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "don t forget there is more than forgetting new metrics for continual learning"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "pseudo recursal solving the catastrophic forgetting problem in deep neural networks"
    - "progressive neural networks"
    - "deep image prior"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-2 (induced by source paper "gap generalizable approximate graph partitioning framework"):
  with members:
    - "mean field theory of graph neural networks in graph partitioning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "device placement optimization with reinforcement learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "semi supervised classification with graph convolutional networks"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "improving graph convolutional networks with non parametric activation functions"):
  with members:
    - "convolutional neural networks via node varying graph filters"
    - "geometric deep learning going beyond euclidean data"
    - "learning to represent programs with graphs"
    - "adaptive least mean squares estimation of graph signals"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "semi supervised learning with ladder networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "semisupervised learning on heterogeneous graphs and its applications to facebook news feed"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "fast r cnn"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "projectionnet learning efficient on device deep networks using neural projections"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "smart reply automated response suggestion for email"
    - "revisiting semi supervised learning with graph embeddings"
    - "learning to hash for indexing big data a survey"
    - "compressing neural networks with the hashing trick"
    - "large scale distributed semi supervised learning using streaming approximation"
    - "neural machine translation by jointly learning to align and translate"
    - "compressing deep convolutional networks using vector quantization"

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)