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: "training deep capsule networks"
  Abstract: The capsules of Capsule Networks are collections of neurons that represent an object or part of an object in a parse tree. The output vector of a capsule encodes the so called instantiation parameters of this object (e.g. position, size, or orientation). The routing-by-agreement algorithm routes output vectors from lower level capsules to upper level capsules. This iterative algorithm selects the most appropriate parent capsule so that the active capsules in the network represent nodes in a parse tree. This parse tree represents the hierarchical composition of objects out of smaller and smaller components. In this paper, we will show experimentally that the routing-by-agreement algorithm does not ensure the emergence of a parse tree in the network. To ensure that all active capsules form a parse tree, we introduce a new routing algorithm called dynamic deep routing. We show that this routing algorithm allows the training of deeper capsule networks and is also more robust to white box adversarial attacks than the original routing algorithm.

Connected through hyperedge HE-1 (induced by source paper "grouping capsules based different types"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "capsule routing via variational bayes"
    - "assessing capsule networks with biased data"
    - "the multi lane capsule network mlcn"
    - "deep learning for classical japanese literature"
    - "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"

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)