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: "robust classification with convolutional prototype learning"
  Abstract: Convolutional neural networks (CNNs) have been widely used for image classification. Despite its high accuracies, CNN has been shown to be easily fooled by some adversarial examples, indicating that CNN is not robust enough for pattern classification. In this paper, we argue that the lack of robustness for CNN is caused by the softmax layer, which is a totally discriminative model and based on the assumption of closed world (i.e., with a fixed number of categories). To improve the robustness, we propose a novel learning framework called convolutional prototype learning (CPL). The advantage of using prototypes is that it can well handle the open world recognition problem and therefore improve the robustness. Under the framework of CPL, we design multiple classification criteria to train the network. Moreover, a prototype loss (PL) is proposed as a regularization to improve the intra-class compactness of the feature representation, which can be viewed as a generative model based on the Gaussian assumption of different classes. Experiments on several datasets demonstrate that CPL can achieve comparable or even better results than traditional CNN, and from the robustness perspective, CPL shows great advantages for both the rejection and incremental category learning tasks.

Connected through hyperedge HE-1 (induced by source paper "domain segmentation and adjustment for generalized zero shot learning"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "generative dual adversarial network for generalized zero shot learning"
    - "learning class prototypes via structure alignment for zero shot recognition"
    - "deep anomaly detection with outlier exposure"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "preserving semantic relations for zero shot learning"
    - "generalized zero shot recognition based on visually semantic embedding"

Connected through hyperedge HE-2 (induced by source paper "visual and semantic prototypes jointly guided cnn for generalized zero shot learning"):
  with members:
    - "multi modal ensemble classification for generalized zero shot learning"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "reducing network agnostophobia"
    - "zero shot kernel learning"
    - "towards effective deep embedding for zero shot learning"
    - "bi adversarial auto encoder for zero shot learning"
    - "preserving semantic relations for zero shot learning"

Connected through hyperedge HE-3 (induced by source paper "hyperspherical prototype networks"):
  with members:
    - "prototypical metric transfer learning for continuous speech keyword spotting with limited training data"
    - "infinite mixture prototypes for few shot learning"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "deep learning on small datasets without pre training using cosine loss"
    - "decoupled networks"
    - "fix your classifier the marginal value of training the last weight layer"
    - "cosface large margin cosine loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"

Connected through hyperedge HE-4 (induced by source paper "learning discriminative features via weights biased softmax loss"):
  with members:
    - "group normalization"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mask r cnn"
    - "instance normalization the missing ingredient for fast stylization"
    - "yolo9000 better faster stronger"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "p odn prototype based open deep network for open set recognition"):
  with members:
    - "generative openmax for multi class open set classification"
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "learning long term dependencies for action recognition with a biologically inspired deep network"
    - "temporal segment networks towards good practices for deep action recognition"
    - "tensorflow a system for large scale machine learning"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "towards open set deep networks"
    - "towards open world recognition"

Connected through hyperedge HE-6 (induced by source paper "tdapnet prototype network with recurrent top down attention for robust object classification under partial occlusion"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "compositional convolutional networks for robust object classification under occlusion"
    - "robustness of object recognition under extreme occlusion in humans and computational models"
    - "convolutional neural networks with alternately updated clique"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "prototypical networks for few shot learning"
    - "learning with rethinking recurrently improving convolutional neural networks through feedback"
    - "visual concepts and compositional voting"

Connected through hyperedge HE-7 (induced by source paper "meta neighborhoods"):
  with members:
    - "meta curvature"
    - "adaptive posterior learning few shot learning with a surprise based memory module"
    - "self supervised generalisation with meta auxiliary learning"
    - "a closer look at few shot classification"
    - "meta learning with differentiable closed form solvers"
    - "memory matching networks for one shot image recognition"
    - "prototypical networks for few shot learning"
    - "learning to remember rare events"

Connected through hyperedge HE-8 (induced by source paper "prototype based neural network layers incorporating vector quantization"):
  with members:
    - "towards the first adversarially robust neural network model on mnist"
    - "provable robustness of relu networks via maximization of linear regions"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "dynamic routing between capsules"
    - "searching for activation functions"
    - "swish a self gated activation function"
    - "formal guarantees on the robustness of a classifier against adversarial manipulation"

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)