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: "deep k nearest neighbors towards confident interpretable and robust deep learning"
  Abstract: Deep neural networks (DNNs) enable innovative applications of machine learning like image recognition, machine translation, or malware detection. However, deep learning is often criticized for its lack of robustness in adversarial settings (e.g., vulnerability to adversarial inputs) and general inability to rationalize its predictions. In this work, we exploit the structure of deep learning to enable new learning-based inference and decision strategies that achieve desirable properties such as robustness and interpretability. We take a first step in this direction and introduce the Deep k-Nearest Neighbors (DkNN). This hybrid classifier combines the k-nearest neighbors algorithm with representations of the data learned by each layer of the DNN: a test input is compared to its neighboring training points according to the distance that separates them in the representations. We show the labels of these neighboring points afford confidence estimates for inputs outside the model's training manifold, including on malicious inputs like adversarial examples--and therein provides protections against inputs that are outside the models understanding. This is because the nearest neighbors can be used to estimate the nonconformity of, i.e., the lack of support for, a prediction in the training data. The neighbors also constitute human-interpretable explanations of predictions. We evaluate the DkNN algorithm on several datasets, and show the confidence estimates accurately identify inputs outside the model, and that the explanations provided by nearest neighbors are intuitive and useful in understanding model failures.

Connected through hyperedge HE-1 (induced by source paper "robustness for non parametric classification a generic attack and defense"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "robust decision trees against adversarial examples"
    - "on the robustness of deep k nearest neighbors"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "on evaluating adversarial robustness"
    - "adversarial training with voronoi constraints"
    - "training for faster adversarial robustness verification via inducing relu stability"

Connected through hyperedge HE-2 (induced by source paper "understanding the decision boundary of deep neural networks an empirical study"):
  with members:
    - "addressing model vulnerability to distributional shifts over image transformation sets"
    - "cross entropy loss and low rank features have responsibility for adversarial examples"
    - "testing robustness against unforeseen adversaries"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the attack generator a systematic approach towards constructing adversarial attacks"
    - "deep skinny neural networks are not universal approximators"
    - "towards the first adversarially robust neural network model on mnist"
    - "adversarial vulnerability for any classifier"

Connected through hyperedge HE-3 (induced by source paper "differentiable top k operator with optimal transport"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "reparameterizable subset sampling via continuous relaxations"
    - "fine grained video categorization with redundancy reduction attention"
    - "neural nearest neighbors networks"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "optnet differentiable optimization as a layer in neural networks"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-4 (induced by source paper "minimum norm adversarial examples on knn and knn based models"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "adversarial examples for non parametric methods attacks defenses and large sample limits"
    - "defending against adversarial examples with k nearest neighbor"
    - "fast rates for a knn classifier robust to unknown asymmetric label noise"
    - "adversarial training with voronoi constraints"
    - "towards the first adversarially robust neural network model on mnist"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "improving entity linking by modeling latent entity type information"):
  with members:
    - "joint learning of named entity recognition and entity linking"
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "zero shot open entity typing as type compatible grounding"
    - "boosting entity linking performance by leveraging unlabeled documents"
    - "learning dynamic context augmentation for global entity linking"
    - "distant learning for entity linking with automatic noise detection"
    - "improving entity linking by modeling latent relations between mentions"
    - "ultra fine entity typing"

Connected through hyperedge HE-6 (induced by source paper "deep k nn defense against clean label data poisoning attacks"):
  with members:
    - "transfer learning for hate speech detection in social media"
    - "defending neural backdoors via generative distribution modeling"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "robustness via curvature regularization and vice versa"
    - "spectral signatures in backdoor attacks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "dual path networks"

Connected through hyperedge HE-7 (induced by source paper "mpc guided imitation learning of neural network policies for the artificial pancreas"):
  with members:
    - "pac confidence sets for deep neural networks via calibrated prediction"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "safe end to end imitation learning for model predictive control"
    - "representation and reinforcement learning for personalized glycemic control in septic patients"
    - "dropoutdagger a bayesian approach to safe imitation learning"
    - "data driven robust control for type 1 diabetes under meal and exercise uncertainties"
    - "generative adversarial imitation learning"
    - "plato policy learning using adaptive trajectory optimization"

Connected through hyperedge HE-8 (induced by source paper "real time out of distribution detection in learning enabled cyber physical systems"):
  with members:
    - "attacking vision based perception in end to end autonomous driving models"
    - "deep anomaly detection with outlier exposure"
    - "robustness to out of distribution inputs via task aware generative uncertainty"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "carla an open urban driving simulator"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

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)