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: "continuous integration of machine learning models with ease ml ci towards a rigorous yet practical treatment"
  Abstract: Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development. Developing a machine learning model is no difference - it is an engineering process with a life cycle, including design, implementation, tuning, testing, and deployment. However, most, if not all, existing continuous integration engines do not support machine learning as first-class citizens. #R##N#In this paper, we present ease.ml/ci, to our best knowledge, the first continuous integration system for machine learning. The challenge of building ease.ml/ci is to provide rigorous guarantees, e.g., single accuracy point error tolerance with 0.999 reliability, with a practical amount of labeling effort, e.g., 2K labels per test. We design a domain specific language that allows users to specify integration conditions with reliability constraints, and develop simple novel optimizations that can lower the number of labels required by up to two orders of magnitude for test conditions popularly used in real production systems.

Connected through hyperedge HE-1 (induced by source paper "model assertions for monitoring and improving ml model"):
  with members:
    - "better ai through logical scaffolding"
    - "outside the box abstraction based monitoring of neural networks"
    - "scaling video analytics on constrained edge nodes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "vstore a data store for analytics on large videos"
    - "selection via proxy efficient data selection for deep learning"
    - "formal security analysis of neural networks using symbolic intervals"
    - "formal verification of neural network controlled autonomous systems"

Connected through hyperedge HE-2 (induced by source paper "quantitative overfitting management for human in the loop ml application development with ease ml meter"):
  with members:
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "deep contextualized word representations"
    - "patient risk assessment and warning symptom detection using deep attention based neural networks"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "snorkel rapid training data creation with weak supervision"
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "mllib machine learning in apache spark"
    - "preventing false discovery in interactive data analysis is hard"

Connected through hyperedge HE-3 (induced by source paper "studying software engineering patterns for designing machine learning systems"):
  with members:
    - "landscape of iot patterns"
    - "machine learning software engineering in practice an industrial case study"
    - "deep convolutional neural network design patterns"

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)