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 network encapsulation"
  Abstract: A capsule is a collection of neurons which represents different variants of a pattern in the network. The routing scheme ensures only certain capsules which resemble lower counterparts in the higher layer should be activated. However, the computational complexity becomes a bottleneck for scaling up to larger networks, as lower capsules need to correspond to each and every higher capsule. To resolve this limitation, we approximate the routing process with two branches: a master branch which collects primary information from its direct contact in the lower layer and an aide branch that replenishes master based on pattern variants encoded in other lower capsules. Compared with previous iterative and unsupervised routing scheme, these two branches are communicated in a fast, supervised and one-time pass fashion. The complexity and runtime of the model are therefore decreased by a large margin. Motivated by the routing to make higher capsule have agreement with lower capsule, we extend the mechanism as a compensation for the rapid loss of information in nearby layers. We devise a feedback agreement unit to send back higher capsules as feedback. It could be regarded as an additional regularization to the network. The feedback agreement is achieved by comparing the optimal transport divergence between two distributions (lower and higher capsules). Such an add-on witnesses a unanimous gain in both capsule and vanilla networks. Our proposed EncapNet performs favorably better against previous state-of-the-arts on CIFAR10/100, SVHN and a subset of ImageNet.

Connected through hyperedge HE-1 (induced by source paper "capsules with inverted dot product attention routing"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "attention routing between capsules"
    - "deep equilibrium models"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "dense and diverse capsule networks making the capsules learn better"
    - "attention is all you need"
    - "dynamic routing between capsules"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "feature intertwiner for object detection"):
  with members:
    - "finding task relevant features for few shot learning by category traversal"
    - "gradient harmonized single stage detector"
    - "grid r cnn"
    - "learning monocular depth by distilling cross domain stereo networks"
    - "improving gans using optimal transport"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "sphereface deep hypersphere embedding for face recognition"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "recursive visual sound separation using minus plus net"):
  with members:
    - "feature intertwiner for object detection"
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "a neural compositional paradigm for image captioning"
    - "move forward and tell a progressive generator of video descriptions"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual event localization in unconstrained videos"

Connected through hyperedge HE-4 (induced by source paper "finding task relevant features for few shot learning by category traversal"):
  with members:
    - "a closer look at few shot classification"
    - "image deformation meta networks for one shot learning"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "incremental few shot learning with attention attractor networks"
    - "improving generalization via scalable neighborhood component analysis"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-5 (induced by source paper "p capsnets a general form of convolutional neural networks"):
  with members:
    - "capsule routing via variational bayes"
    - "attention routing between capsules"
    - "capsule networks need an improved routing algorithm"
    - "generalized capsule networks with trainable routing procedure"
    - "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-6 (induced by source paper "improving the robustness of capsule networks to image affine transformations"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "attention routing between capsules"
    - "generalized capsule networks with trainable routing procedure"
    - "group equivariant capsule networks"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "sparse unsupervised capsules generalize better"
    - "capsgan using dynamic routing for generative adversarial networks"
    - "dynamic routing between capsules"

Connected through hyperedge HE-7 (induced by source paper "attention routing between capsules"):
  with members:
    - "attention is all you need"
    - "dynamic routing between capsules"
    - "densely connected convolutional networks"
    - "effective approaches to attention based neural machine translation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "training very deep networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "adaptive routing between capsules"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "group equivariant capsule networks"
    - "dynamic routing between capsules"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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)