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: "low rank approximations for conditional feedforward computation in deep neural networks"
  Abstract: Scalability properties of deep neural networks raise key research questions, particularly as the problems considered become larger and more challenging. This paper expands on the idea of conditional computation introduced by Bengio, et. al., where the nodes of a deep network are augmented by a set of gating units that determine when a node should be calculated. By factorizing the weight matrix into a low-rank approximation, an estimation of the sign of the pre-nonlinearity activation can be efficiently obtained. For networks using rectified-linear hidden units, this implies that the computation of a hidden unit with an estimated negative pre-nonlinearity can be ommitted altogether, as its value will become zero when nonlinearity is applied. For sparse neural networks, this can result in considerable speed gains. Experimental results using the MNIST and SVHN data sets with a fully-connected deep neural network demonstrate the performance robustness of the proposed scheme with respect to the error introduced by the conditional computation process.

Connected through hyperedge HE-1 (induced by source paper "attention over parameters for dialogue systems"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "global to local memory pointer networks for task oriented dialogue"
    - "moel mixture of empathetic listeners"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "caire an end to end empathetic chatbot"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "transferable multi domain state generator for task oriented dialogue systems"

Connected through hyperedge HE-2 (induced by source paper "controlling computation versus quality for neural sequence models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "reducing transformer depth on demand with structured dropout"
    - "mixture models for diverse machine translation tricks of the trade"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "universal transformers"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "condconv conditionally parameterized convolutions for efficient inference"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "mixconv mixed depthwise convolutional kernels"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "splinenets continuous neural decision graphs"
    - "neural architecture optimization"

Connected through hyperedge HE-4 (induced by source paper "routing networks and the challenges of modular and compositional computation"):
  with members:
    - "a meta mdp approach to exploration for lifelong reinforcement learning"
    - "reinforcement learning without backpropagation or a clock"
    - "the termination critic"
    - "modular networks learning to decompose neural computation"
    - "learning abstract options"
    - "evolutionary architecture search for deep multitask networks"
    - "modular meta learning"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-5 (induced by source paper "learning sparse mixture of experts for visual question answering"):
  with members:
    - "modularity matters learning invariant relational reasoning tasks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "adaptive neural networks for efficient inference"
    - "a simple neural network module for relational reasoning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "multi scale dense networks for resource efficient image classification"
    - "squeeze and excitation networks"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"

Connected through hyperedge HE-6 (induced by source paper "maskconnect connectivity learning by gradient descent"):
  with members:
    - "multi fiber networks for video recognition"
    - "efficient neural architecture search via parameter sharing"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "large scale evolution of image classifiers"
    - "blockdrop dynamic inference paths in residual networks"
    - "hierarchical representations for efficient architecture search"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-7 (induced by source paper "adaptive neural trees"):
  with members:
    - "multi layered gradient boosting decision trees"
    - "continuously constructive deep neural networks"
    - "ndt neual decision tree towards fully functioned neural graph"
    - "large scale evolution of image classifiers"
    - "lifelong learning with dynamically expandable networks"
    - "efficient architecture search by network transformation"
    - "energy efficient amortized inference with cascaded deep classifiers"
    - "efficient multitask feature and relationship learning"

Connected through hyperedge HE-8 (induced by source paper "you look twice gaternet for dynamic filter selection in cnns"):
  with members:
    - "learning sparse deep feedforward networks via tree skeleton expansion"
    - "discrete autoencoders for sequence models"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning transferable architectures for scalable image recognition"
    - "convolutional networks with adaptive inference graphs"
    - "improved regularization of convolutional neural networks with cutout"
    - "shake shake regularization"
    - "inception v4 inception resnet and the impact of residual connections on 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.LG(Machine Learning)