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: "improved explainability of capsule networks relevance path by agreement"
  Abstract: Recent advancements in signal processing and machine learning domains have resulted in an extensive surge of interest in deep learning models due to their unprecedented performance and high accuracy for different and challenging problems of significant engineering importance. However, when such deep learning architectures are utilized for making critical decisions such as the ones that involve human lives (e.g., in medical applications), it is of paramount importance to understand, trust, and in one word "explain" the rational behind deep models' decisions. Currently, deep learning models are typically considered as black-box systems, which do not provide any clue on their internal processing actions. Although some recent efforts have been initiated to explain behavior and decisions of deep networks, explainable artificial intelligence (XAI) domain is still in its infancy. In this regard, we consider capsule networks (referred to as CapsNets), which are novel deep structures; recently proposed as an alternative counterpart to convolutional neural networks (CNNs), and posed to change the future of machine intelligence. In this paper, we investigate and analyze structures and behaviors of the CapsNets and illustrate potential explainability properties of such networks. Furthermore, we show possibility of transforming deep learning architectures in to transparent networks via incorporation of capsules in different layers instead of convolution layers of the CNNs.

Connected through hyperedge HE-1 (induced by source paper "examining the benefits of capsule neural networks"):
  with members:
    - "convolutional capsule network for classification of breast cancer histology images"
    - "videocapsulenet a simplified network for action detection"
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "compositional coding capsule network with k means routing for text classification"
    - "a capsule network for traffic speed prediction in complex road networks"

Connected through hyperedge HE-2 (induced by source paper "efficiency and scalability of multi lane capsule networks mlcn"):
  with members:
    - "path capsule networks"
    - "capsnet comparative performance evaluation for image classification"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "beyond data and model parallelism for deep neural networks"
    - "fast capsnet for lung cancer screening"
    - "compositional coding capsule network with k means routing for text classification"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "a capsule network for traffic speed prediction in complex road networks"

Connected through hyperedge HE-3 (induced by source paper "an effective hit or miss layer favoring feature interpretation as learned prototypes deformations"):
  with members:
    - "object localization and motion transfer learning with capsules"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "brain tumor type classification via capsule networks"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "sparse unsupervised capsules generalize better"
    - "dynamic routing between capsules"
    - "interpretable convolutional neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "the multi lane capsule network mlcn"):
  with members:
    - "path capsule networks"
    - "capsnet comparative performance evaluation for image classification"
    - "fast capsnet for lung cancer screening"
    - "compositional coding capsule network with k means routing for text classification"
    - "capsule networks against medical imaging data challenges"
    - "dynamic routing between capsules"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"):
  with members:
    - "object localization and motion transfer learning with capsules"
    - "an attention based bi gru capsnet model for hypernymy detection between compound entities"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "sparse unsupervised capsules generalize better"
    - "dynamic routing between capsules"
    - "dropmax adaptive variational softmax"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "evaluation of neural networks for image recognition applications designing a 0 1 milp model of a cnn to create adversarials"):
  with members:
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "backpropagation generalized for output derivatives"
    - "dynamic routing between capsules"
    - "an overview of gradient descent optimization algorithms"
    - "deep residual learning for image recognition"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

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.CV(Computer Vision and Pattern Recognition)