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: "streamed learning one pass svms"
  Abstract: We present a streaming model for large-scale classification (in the context of $\ell_2$-SVM) by leveraging connections between learning and computational geometry. The streaming model imposes the constraint that only a single pass over the data is allowed. The $\ell_2$-SVM is known to have an equivalent formulation in terms of the minimum enclosing ball (MEB) problem, and an efficient algorithm based on the idea of \emph{core sets} exists (Core Vector Machine, CVM). CVM learns a $(1+\varepsilon)$-approximate MEB for a set of points and yields an approximate solution to corresponding SVM instance. However CVM works in batch mode requiring multiple passes over the data. This paper presents a single-pass SVM which is based on the minimum enclosing ball of streaming data. We show that the MEB updates for the streaming case can be easily adapted to learn the SVM weight vector in a way similar to using online stochastic gradient updates. Our algorithm performs polylogarithmic computation at each example, and requires very small and constant storage. Experimental results show that, even in such restrictive settings, we can learn efficiently in just one pass and get accuracies comparable to other state-of-the-art SVM solvers (batch and online). We also give an analysis of the algorithm, and discuss some open issues and possible extensions.

Connected through hyperedge HE-1 (induced by source paper "on coresets for support vector machines"):
  with members:
    - "provable filter pruning for efficient neural networks"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "new frameworks for offline and streaming coreset constructions"
    - "accurate streaming support vector machines"
    - "fast svm training using approximate extreme points"
    - "a unified framework for approximating and clustering data"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-2 (induced by source paper "budgeted training rethinking deep neural network training under resource constraints"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "learnable embedding space for efficient neural architecture compression"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "deep rigid instance scene flow"
    - "gradient descent finds global minima of deep neural networks"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "darts differentiable architecture search"

Connected through hyperedge HE-3 (induced by source paper "coresets for minimum enclosing balls over sliding windows"):
  with members:
    - "efficient representative subset selection over sliding windows"
    - "near optimal coresets of kernel density estimates"
    - "real time influence maximization on dynamic social streams"
    - "coresets for kernel regression"
    - "submodular optimization over sliding windows"

Connected through hyperedge HE-4 (induced by source paper "recurring concept meta learning for evolving data streams"):
  with members:
    - "hellinger distance trees for imbalanced streams"

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)