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: "learning with feature evolvable streams"
  Abstract: Learning with streaming data has attracted much attention during the past few years. Though most studies consider data stream with fixed features, in real practice the features may be evolvable. For example, features of data gathered by limited-lifespan sensors will change when these sensors are substituted by new ones. In this paper, we propose a novel learning paradigm: \emph{Feature Evolvable Streaming Learning} where old features would vanish and new features would occur. Rather than relying on only the current features, we attempt to recover the vanished features and exploit it to improve performance. Specifically, we learn two models from the recovered features and the current features, respectively. To benefit from the recovered features, we develop two ensemble methods. In the first method, we combine the predictions from two models and theoretically show that with the assistance of old features, the performance on new features can be improved. In the second approach, we dynamically select the best single prediction and establish a better performance guarantee when the best model switches. Experiments on both synthetic and real data validate the effectiveness of our proposal.

Connected through hyperedge HE-1 (induced by source paper "dual averaging method for online graph structured sparsity"):
  with members:
    - "introduction to online convex optimization"
    - "learning in non convex games with an optimization oracle"
    - "efficient regret minimization in non convex games"
    - "graph sparse logistic regression"
    - "unregularized online learning algorithms with general loss functions"
    - "adam a method for stochastic optimization"
    - "approximation algorithms for model based compressive sensing"
    - "gradient hard thresholding pursuit for sparsity constrained optimization"

Connected through hyperedge HE-2 (induced by source paper "prediction with unpredictable feature evolution"):
  with members:
    - "one pass learning with incremental and decremental features"
    - "frequent directions simple and deterministic matrix sketching"
    - "a survey on multi view learning"
    - "simple and deterministic matrix sketching"
    - "a simpler approach to matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "active feature acquisition with supervised matrix completion"):
  with members:
    - "matrix completion with queries"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "high rank matrix completion and subspace clustering with missing data"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "uniqueness of low rank matrix completion by rigidity theory"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"
    - "active learning with statistical models"

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)