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: "compositional falsification of cyber physical systems with machine learning components"
  Abstract: Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small adversarial perturbations. This raises the question: can the output from learning components can lead to a failure of the entire CPS? In this work, we address this question by formulating it as a problem of falsifying signal temporal logic (STL) specifications for CPS with ML components. We propose a compositional falsification framework where a temporal logic falsifier and a machine learning analyzer cooperate with the aim of finding falsifying executions of the considered model. The efficacy of the proposed technique is shown on an automatic emergency braking system model with a perception component based on deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "teaching temporal logics to neural networks"):
  with members:
    - "mathematical reasoning in latent space"
    - "guiding high performance sat solvers with unsat core predictions"
    - "enhancing the transformer with explicit relational encoding for math problem solving"
    - "analysing mathematical reasoning abilities of neural models"
    - "deep learning for symbolic mathematics"
    - "graph representations for higher order logic and theorem proving"
    - "holist an environment for machine learning of higher order theorem proving"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "reliability validation of learning enabled vehicle tracking"):
  with members:
    - "test metrics for recurrent neural networks"
    - "reasoning about cognitive trust in stochastic multiagent systems"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "detecting and tracking small moving objects in wide area motion imagery wami using convolutional neural networks cnns"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepmutation mutation testing of deep learning systems"

Connected through hyperedge HE-3 (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 k nearest neighbors towards confident interpretable and robust deep learning"
    - "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"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-4 (induced by source paper "end to end robustness for sensing reasoning machine learning pipelines"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "joint inference of reward machines and policies for reinforcement learning"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "joint inference of multiple label types in large networks"

Connected through hyperedge HE-5 (induced by source paper "testing and verification of neural network based safety critical control software a systematic literature review"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "local rule based explanations of black box decision systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "experimental resilience assessment of an open source driving agent"
    - "interpreting deep classifier by visual distillation of dark knowledge"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "software engineering challenges of deep learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-6 (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"
    - "runtime monitoring neuron activation patterns"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "a formalization of robustness for deep neural networks"):
  with members:
    - "counterexample guided data augmentation"
    - "semantic adversarial deep learning"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "systematic testing of convolutional neural networks for autonomous driving"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "feature guided black box safety testing of deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "analyzing cyber physical systems from the perspective of artificial intelligence"):
  with members:
    - "adversarial resilience learning towards systemic vulnerability analysis for large and complex systems"
    - "gray box adversarial testing for control systems with machine learning component"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "co simulation state of the art"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "practical black box attacks against machine learning"
    - "towards verified artificial intelligence"

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.SY(Systems and Control)