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: "runtime monitoring neuron activation patterns"
  Abstract: For using neural networks in safety critical domains, it is important to know if a decision made by a neural network is supported by prior similarities in training. We propose runtime neuron activation pattern monitoring - after the standard training process, one creates a monitor by feeding the training data to the network again in order to store the neuron activation patterns in abstract form. In operation, a classification decision over an input is further supplemented by examining if a pattern similar (measured by Hamming distance) to the generated pattern is contained in the monitor. If the monitor does not contain any pattern similar to the generated pattern, it raises a warning that the decision is not based on the training data. Our experiments show that, by adjusting the similarity-threshold for activation patterns, the monitors can report a significant portion of misclassfications to be not supported by training with a small false-positive rate, when evaluated on a test set.

Connected through hyperedge HE-1 (induced by source paper "engineering problems in machine learning systems"):
  with members:
    - "improving transparency of deep neural inference process"
    - "towards dependability metrics for neural networks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "quantitative projection coverage for testing ml enabled autonomous systems"
    - "towards proving the adversarial robustness of deep neural networks"
    - "maximum resilience of artificial neural networks"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "an analysis of iso 26262 using machine learning safely in automotive software"

Connected through hyperedge HE-2 (induced by source paper "nn dependability kit engineering neural networks for safety critical autonomous driving systems"):
  with members:
    - "scenic a language for scenario specification and scene generation"
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "towards dependability metrics for neural networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "architecting dependable learning enabled autonomous systems a survey"):
  with members:
    - "interpreting cnns via decision trees"
    - "conditional affordance learning for driving in urban environments"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "safety and trustworthiness of deep neural networks a survey"
    - "naturalistic driver intention and path prediction using recurrent neural networks"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-4 (induced by source paper "organization of ml based product development as per iso 26262"):
  with members:
    - "self driving cars a survey"
    - "a review of deep learning with special emphasis on architectures applications and recent trends"
    - "nn dependability kit engineering neural networks for safety critical systems"
    - "active learning for deep object detection"
    - "using machine learning safely in automotive software an assessment and adaption of software process requirements in iso 26262"
    - "lanenet real time lane detection networks for autonomous driving"
    - "a survey of safety and trustworthiness of deep neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-5 (induced by source paper "outside the box abstraction based monitoring of neural networks"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "end to end learning for self driving cars"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "end to end learning of driving models from large scale video datasets"
    - "the octagon abstract domain"

Connected through hyperedge HE-6 (induced by source paper "detection and mitigation of rare subclasses in neural network classifiers"):
  with members:
    - "assuring the machine learning lifecycle desiderata methods and challenges"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "fairness through computationally bounded awareness"
    - "fairtest discovering unwarranted associations in data driven applications"
    - "fairness through awareness"

Connected through hyperedge HE-7 (induced by source paper "nn dependability kit engineering neural networks for safety critical systems"):
  with members:
    - "scenic a language for scenario specification and scene generation"
    - "a dual approach to scalable verification of deep networks"
    - "towards dependability metrics for neural networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "conditional affordance learning for driving in urban environments"
    - "concolic testing for deep neural networks"
    - "testing deep neural networks"
    - "quantitative projection coverage for testing ml enabled autonomous systems"

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)