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: "safetynets verifiable execution of deep neural networks on an untrusted cloud"
  Abstract: Inference using deep neural networks is often outsourced to the cloud since it is a computationally demanding task. However, this raises a fundamental issue of trust. How can a client be sure that the cloud has performed inference correctly? A lazy cloud provider might use a simpler but less accurate model to reduce its own computational load, or worse, maliciously modify the inference results sent to the client. We propose SafetyNets, a framework that enables an untrusted server (the cloud) to provide a client with a short mathematical proof of the correctness of inference tasks that they perform on behalf of the client. Specifically, SafetyNets develops and implements a specialized interactive proof (IP) protocol for verifiable execution of a class of deep neural networks, i.e., those that can be represented as arithmetic circuits. Our empirical results on three- and four-layer deep neural networks demonstrate the run-time costs of SafetyNets for both the client and server are low. SafetyNets detects any incorrect computations of the neural network by the untrusted server with high probability, while achieving state-of-the-art accuracy on the MNIST digit recognition (99.4%) and TIMIT speech recognition tasks (75.22%).

Connected through hyperedge HE-1 (induced by source paper "the relationship between trust in ai and trustworthy machine learning technologies"):
  with members:
    - "decision provenance harnessing data flow for accountable systems"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "methods for interpreting and understanding deep neural networks"
    - "sok security and privacy in machine learning"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "interpretable explorable approximations of black box models"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-2 (induced by source paper "veriml enabling integrity assurances and fair payments for machine learning as a service"):
  with members:
    - "a zero knowledge sumcheck and its applications"
    - "noisy activation functions"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"
    - "time optimal interactive proofs for circuit evaluation"

Connected through hyperedge HE-3 (induced by source paper "checknet secure inference on untrusted devices"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "security for machine learning based systems attacks and challenges during training and inference"):
  with members:
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "deepapt nation state apt attribution using end to end deep neural networks"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "cryptodl deep neural networks over encrypted data"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "is deep learning safe for robot vision adversarial examples against the icub humanoid"
    - "modular learning component attacks today s reality tomorrow s challenge"

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)