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: "gated networks an inventory"
  Abstract: Gated networks are networks that contain gating connections, in which the outputs of at least two neurons are multiplied. Initially, gated networks were used to learn relationships between two input sources, such as pixels from two images. More recently, they have been applied to learning activity recognition or multi-modal representations. The aims of this paper are threefold: 1) to explain the basic computations in gated networks to the non-expert, while adopting a standpoint that insists on their symmetric nature. 2) to serve as a quick reference guide to the recent literature, by providing an inventory of applications of these networks, as well as recent extensions to the basic architecture. 3) to suggest future research directions and applications.

Connected through hyperedge HE-1 (induced by source paper "batch shaping for learning conditional channel gated networks"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "sgad soft guided adaptively dropped neural network"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "compressing neural networks using the variational information bottleneck"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "dynamic channel pruning feature boosting and suppression"

Connected through hyperedge HE-2 (induced by source paper "slice based learning a programming model for residual learning in critical data slices"):
  with members:
    - "overton a data system for monitoring and improving machine learned products"
    - "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"
    - "probing what different nlp tasks teach machines about function word comprehension"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "deeper broader and artier domain generalization"):
  with members:
    - "learning feed forward one shot learners"
    - "deep multi task representation learning a tensor factorisation approach"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "domain separation networks"
    - "learning transferable features with deep adaptation networks"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "domain generalization for object recognition with multi task autoencoders"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "unifying multi domain multi task learning tensor and neural network perspectives"):
  with members:
    - "deep multi task representation learning a tensor factorisation approach"
    - "trace norm regularised deep multi task learning"
    - "zero shot learning through cross modal transfer"
    - "learning task grouping and overlap in multi task learning"
    - "frustratingly easy domain adaptation"

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)