human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "geometric online adaptation graph based osfs for streaming samples"):
  Abstract: Feature selection seeks a curated subset of available features such that they contain sufficient discriminative information for a given learning task. Online streaming feature selection (OSFS) further extends this to the streaming scenario where the model gets only a single pass at features, one at a time. While this problem setting allows for training high performance models with low computational and storage requirements, this setting also makes the assumption that there is a fixed number of samples, which is often invalidated in many real-world problems. In this paper, we consider a new setting called Online Streaming Feature Selection with Streaming Samples (OSFS-SS) with a fixed class label space, where both the features and the samples are simultaneously streamed. We extend the state-of-the-art OSFS method to work in this setting. Furthermore, we introduce a novel algorithm, that has applications in both the OSFS and OSFS-SS settings, called Geometric Online Adaptation (GOA) which uses a graph-based class conditional geometric dependency (CGD) criterion to measure feature relevance and maintain a minimal feature subset with relatively high classification performance. We evaluate the proposed GOA algorithm on both simulation and real world datasets highlighting how in both the OSFS and OSFS-SS settings it achieves higher performance while maintaining smaller feature subsets than relevant baselines.

Includes members:
  Member "geometric estimation of multivariate dependency":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mint deep network compression via mutual information based neuron trimming")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "online unsupervised multi view feature selection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a geometric approach to online streaming feature selection")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)