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: "automated machine learning via admm"
  Abstract: We study the automated machine learning (AutoML) problem of jointly selecting appropriate algorithms from an algorithm portfolio as well as optimizing their hyper-parameters for certain learning tasks. The main challenges include a) the coupling between algorithm selection and hyper-parameter optimization (HPO), and b) the black-box optimization nature of the problem where the optimizer cannot access the gradients of the loss function but may query function values. To circumvent these difficulties, we propose a new AutoML framework by leveraging the alternating direction method of multipliers (ADMM) scheme. Due to the splitting properties of ADMM, algorithm selection and HPO can be decomposed through the augmented Lagrangian function. As a result, HPO with mixed continuous and integer constraints are efficiently handled through a query-efficient Bayesian optimization approach and Euclidean projection operator that yields a closed-form solution. Algorithm selection in ADMM is naturally interpreted as a combinatorial bandit problem. The effectiveness of our proposed methodology is compared to state-of-the-art AutoML schemes such as TPOT and Auto-sklearn on numerous benchmark data sets.

Connected through hyperedge HE-1 (induced by source paper "how can ai automate end to end data science"):
  with members:
    - "meta learning a survey"
    - "incorporating behavioral constraints in online ai systems"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "autoaugment learning augmentation policies from data"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-2 (induced by source paper "how data scientistswork together with domain experts in scientific collaborations to find the right answer or to ask the right question"):
  with members:
    - "slack channels ecology in enterprises how employees collaborate through group chat"
    - "a formal method for automl via admm"
    - "openml networked science in machine learning"

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)