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: "generalized fisher score for feature selection"
  Abstract: Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature independently according to their scores under the Fisher criterion, which leads to a suboptimal subset of features. In this paper, we present a generalized Fisher score to jointly select features. It aims at finding an subset of features, which maximize the lower bound of traditional Fisher score. The resulting feature selection problem is a mixed integer programming, which can be reformulated as a quadratically constrained linear programming (QCLP). It is solved by cutting plane algorithm, in each iteration of which a multiple kernel learning problem is solved alternatively by multivariate ridge regression and projected gradient descent. Experiments on benchmark data sets indicate that the proposed method outperforms Fisher score as well as many other state-of-the-art feature selection methods.

Connected through hyperedge HE-1 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "learning variations in human motion via mix and match perturbation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "learning trajectory dependencies for human motion prediction"
    - "structured prediction helps 3d human motion modelling"
    - "imitation learning for human pose prediction"
    - "predicting 3d human dynamics from video"

Connected through hyperedge HE-2 (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"
    - "infinite latent feature selection a probabilistic latent graph based ranking approach"
    - "feature selection library matlab toolbox"
    - "unsupervised feature selection with adaptive structure learning"

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"
    - "infinite latent feature selection a probabilistic latent graph based ranking approach"

Connected through hyperedge HE-4 (induced by source paper "a new malware detection system using a high performance elm method"):
  with members:
    - "android malware detection using markov chain model of application behaviors in requesting system services"
    - "monet a user oriented behavior based malware variants detection system for android"

Connected through hyperedge HE-5 (induced by source paper "training on the test set an analysis of spampinato et al 31"):
  with members:
    - "improved techniques for training gans"
    - "deep feature learning for eeg recordings"
    - "learning representations from eeg with deep recurrent convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "feature selection a data perspective"):
  with members:
    - "gradient boosted feature selection"
    - "challenges of feature selection for big data analytics"
    - "online feature selection with group structure analysis"
    - "unsupervised feature selection with adaptive structure learning"
    - "attentional neural network feature selection using cognitive feedback"
    - "towards ultrahigh dimensional feature selection for big data"
    - "simple and deterministic matrix sketching"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-7 (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"
    - "infinite latent feature selection a probabilistic latent graph based ranking approach"
    - "unsupervised feature selection with adaptive structure learning"

Connected through hyperedge HE-8 (induced by source paper "infinite latent feature selection a probabilistic latent graph based ranking approach"):
  with members:
    - "feature selection library matlab toolbox"
    - "very deep convolutional networks for large scale image recognition"
    - "probabilistic latent semantic analysis"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

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)