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: "capsule routing via variational bayes"
  Abstract: Capsule Networks are a recently proposed alternative for constructing Neural Networks, and early indications suggest that they can provide greater generalisation capacity using fewer parameters. In capsule networks scalar neurons are replaced with capsule vectors or matrices, whose entries represent different properties of objects. The relationships between objects and its parts are learned via trainable viewpoint-invariant transformation matrices, and the presence of a given object is decided by the level of agreement among votes from its parts. This interaction occurs between capsule layers and is a process called routing-by-agreement. Although promising, capsule networks remain underexplored by the community, and in this paper we present a new capsule routing algorithm based of Variational Bayes for a mixture of transforming gaussians. Our Bayesian approach addresses some of the inherent weaknesses of EM routing such as the 'variance collapse' by modelling uncertainty over the capsule parameters in addition to the routing assignment posterior probabilities. We test our method on public domain datasets and outperform the state-of-the-art performance on smallNORB using 50% less capsules.

Connected through hyperedge HE-1 (induced by source paper "multi source deep domain adaptation for quality control in retail food packaging"):
  with members:
    - "deep coral correlation alignment for deep domain adaptation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "grouping capsules based different types"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "assessing capsule networks with biased data"
    - "the multi lane capsule network mlcn"
    - "deep learning for classical japanese literature"
    - "training deep capsule networks"
    - "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "dense and diverse capsule networks making the capsules learn better"

Connected through hyperedge HE-3 (induced by source paper "p capsnets a general form of convolutional neural networks"):
  with members:
    - "attention routing between capsules"
    - "capsule networks need an improved routing algorithm"
    - "generalized capsule networks with trainable routing procedure"
    - "neural network encapsulation"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "prodsumnet reducing model parameters in deep neural networks via product of sums matrix decompositions"
    - "dense and diverse capsule networks making the capsules learn better"
    - "dynamic routing between capsules"

Connected through hyperedge HE-4 (induced by source paper "nemesyst a hybrid parallelism deep learning based framework applied for internet of things enabled food retailing refrigeration systems"):
  with members:
    - "on the convergence of adam and beyond"
    - "using deep learning to predict plant growth and yield in greenhouse environments"
    - "towards a deep unified framework for nuclear reactor perturbation analysis"
    - "deep learning in agriculture a survey"
    - "deep bayesian self training"
    - "dynamic routing between capsules"
    - "recent trends in deep learning based natural language processing"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

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)