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: "finding invariants in deep neural networks"
  Abstract: We present techniques for automatically inferring invariant properties of feed-forward neural networks. Our insight is that feed forward networks should be able to learn a decision logic that is captured in the activation patterns of its neurons. We propose to extract such decision patterns that can be considered as invariants of the network with respect to a certain output behavior. We present techniques to extract input invariants as convex predicates on the input space, and layer invariants that represent features captured in the hidden layers. We apply the techniques on the networks for the MNIST and ACASXU applications. Our experiments highlight the use of invariants in a variety of applications, such as explainability, providing robustness guarantees, detecting adversaries, simplifying proofs and network distillation.

Connected through hyperedge HE-1 (induced by source paper "finding input characterizations for output properties in relu neural networks"):
  with members:
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "discovering invariances in healthcare neural networks"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "temporal clustering invariance in irregular healthcare time series"
    - "mimic extract a data extraction preprocessing and representation pipeline for mimic iii"
    - "on the convergence of adam and beyond"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "pytorch an imperative style high performance deep learning library"
    - "excessive invariance causes adversarial vulnerability"
    - "scalable and accurate deep learning for electronic health records"

Connected through hyperedge HE-3 (induced by source paper "incremental bounded model checking of artificial neural networks in cuda"):
  with members:
    - "symbolic execution for deep neural networks"
    - "testing deep neural networks"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "convergence analysis of two layer neural networks with relu activation"
    - "end to end learning for self driving cars"

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)