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: "methods of hierarchical clustering"
  Abstract: We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm.

Connected through hyperedge HE-1 (induced by source paper "feature trajectory dynamic time warping for clustering of speech segments"):
  with members:
    - "asr free cnn dtw keyword spotting using multilingual bottleneck features for almost zero resource languages"
    - "fast asr free and almost zero resource keyword spotting using dtw and cnns for humanitarian monitoring"
    - "voice recognition algorithms using mel frequency cepstral coefficient mfcc and dynamic time warping dtw techniques"

Connected through hyperedge HE-2 (induced by source paper "r clustering for egocentric video segmentation"):
  with members:
    - "seeing the big picture deep embedding with contextual evidences"
    - "caffe convolutional architecture for fast feature embedding"
    - "multi digit number recognition from street view imagery using deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "generating object cluster hierarchies for benchmarking"):
  with members:
    - "hierarchical gaussian mixture model with objects attached to terminal and non terminal dendrogram nodes"
    - "hybrid ant colony optimization in solving multi skill resource constrained project scheduling problem"
    - "openml networked science in machine learning"
    - "imagenet large scale visual recognition challenge"
    - "an overview on clustering methods"
    - "bayesian rose trees"

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.IR(Information Retrieval)