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: "feature weighted linear stacking"
  Abstract: Ensemble methods, such as stacking, are designed to boost predictive accuracy by blending the predictions of multiple machine learning models. Recent work has shown that the use of meta-features, additional inputs describing each example in a dataset, can boost the performance of ensemble methods, but the greatest reported gains have come from nonlinear procedures requiring significant tuning and training time. Here, we present a linear technique, Feature-Weighted Linear Stacking (FWLS), that incorporates meta-features for improved accuracy while retaining the well-known virtues of linear regression regarding speed, stability, and interpretability. FWLS combines model predictions linearly using coefficients that are themselves linear functions of meta-features. This technique was a key facet of the solution of the second place team in the recently concluded Netflix Prize competition. Significant increases in accuracy over standard linear stacking are demonstrated on the Netflix Prize collaborative filtering dataset.

Connected through hyperedge HE-1 (induced by source paper "metaselector meta learning for recommendation with user level adaptive model selection"):
  with members:
    - "a closer look at few shot classification"
    - "neural graph collaborative filtering"
    - "meta learning pac bayes priors in model averaging"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "one at a time a meta learning recommender system for recommendation algorithm selection on micro level"
    - "federated meta learning with fast convergence and efficient communication"
    - "neural factorization machines for sparse predictive analytics"

Connected through hyperedge HE-2 (induced by source paper "the nn stacking feature weighted linear stacking through neural networks"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "cltune a generic auto tuner for opencl kernels"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-3 (induced by source paper "on hybrid modular recommendation systems for video streaming"):
  with members:
    - "neural autoregressive collaborative filtering for implicit feedback"
    - "hybrid recommender system based on autoencoders"
    - "matrix completion with queries"

Connected through hyperedge HE-4 (induced by source paper "user sensitive recommendation ensemble with clustered multi task learning"):
  with members:
    - "collaborative filtering with social exposure a modular approach to social recommendation"

Connected through hyperedge HE-5 (induced by source paper "one at a time a meta learning recommender system for recommendation algorithm selection on micro level"):
  with members:
    - "cf4cf recommending collaborative filtering algorithms using collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "using posters to recommend anime and mangas in a cold start scenario"):
  with members:
    - "specializing joint representations for the task of product recommendation"
    - "parvecmf a paragraph vector based matrix factorization recommender system"
    - "combating the cold start user problem in model based collaborative filtering"
    - "metadata embeddings for user and item cold start recommendations"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "on the consistency of ordinal regression methods"

Connected through hyperedge HE-7 (induced by source paper "the monkeytyping solution to the youtube 8m video understanding challenge"):
  with members:
    - "temporal segment networks towards good practices for deep action recognition"
    - "youtube 8m a large scale video classification benchmark"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-8 (induced by source paper "recognizing fine grained and composite activities using hand centric features and script data"):
  with members:
    - "a dataset for movie description"
    - "coherent multi sentence video description with variable level of detail"
    - "weakly supervised action labeling in videos under ordering constraints"
    - "zero shot learning through cross modal transfer"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"
    - "how important are deformable parts in the deformable parts model"

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)