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: "infinite latent feature selection a probabilistic latent graph based ranking approach"
  Abstract: Feature selection is playing an increasingly significant role with respect to many computer vision applications spanning from object recognition to visual object tracking. However, most of the recent solutions in feature selection are not robust across different and heterogeneous set of data. In this paper, we address this issue proposing a robust probabilistic latent graph-based feature selection algorithm that performs the ranking step while considering all the possible subsets of features, as paths on a graph, bypassing the combinatorial problem analytically. An appealing characteristic of the approach is that it aims to discover an abstraction behind low-level sensory data, that is, relevancy. Relevancy is modelled as a latent variable in a PLSA-inspired generative process that allows the investigation of the importance of a feature when injected into an arbitrary set of cues. The proposed method has been tested on ten diverse benchmarks, and compared against eleven state of the art feature selection methods. Results show that the proposed approach attains the highest performance levels across many different scenarios and difficulties, thereby confirming its strong robustness while setting a new state of the art in feature selection domain.

Connected through hyperedge HE-1 (induced by source paper "a matlab toolbox for feature importance ranking"):
  with members:
    - "an effective feature selection method based on pair wise feature proximity for high dimensional low sample size data"
    - "feature selection library matlab toolbox"
    - "unsupervised feature selection with adaptive structure learning"
    - "generalized fisher score for feature selection"

Connected through hyperedge HE-2 (induced by source paper "covariance free partial least squares an incremental dimensionality reduction method"):
  with members:
    - "pruning deep neural networks using partial least squares"
    - "learning multiple visual domains with residual adapters"
    - "unconstrained still video based face verification with deep convolutional neural networks"
    - "fast r cnn"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-3 (induced by source paper "emotion recognition in low resource settings an evaluation of automatic feature selection methods"):
  with members:
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "generalized fisher score for feature selection"

Connected through hyperedge HE-4 (induced by source paper "pruning deep neural networks using partial least squares"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "nisp pruning networks using neuron importance score propagation"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "channel pruning for accelerating very deep neural networks"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "automatic multi objective based feature selection for classification"):
  with members:
    - "hybrid deep neural networks for all cause mortality prediction from ldct images"
    - "interpretable convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "calibrated steganalysis of mp3stego in multi encoder scenario"):
  with members:
    - "decomposition based transfer distance metric learning for image classification"
    - "multi layer architecture for efficient steganalysis of undermp3cover in multi encoder scenario"
    - "comprehensive review of audio steganalysis methods"
    - "universal audio steganalysis based on calibration and reversed frequency resolution of human auditory system"
    - "unsupervised feature selection with adaptive structure learning"
    - "generalized fisher score for feature selection"

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.CV(Computer Vision and Pattern Recognition)