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: "detecting falls with x factor hidden markov models"
  Abstract: Graphical abstractDisplay Omitted HighlightsProposed new X-Factor Hidden Markov Models to identify falls in the absence of their training data.Proposed a novel cross-validation method to optimize parameters in the absence of fall data.Experimentally showed that performance of supervised classifiers deteriorate with very limited training fall data. Identification of falls while performing normal activities of daily living (ADL) is important to ensure personal safety and well-being. However, falling is a short term activity that occurs rarely and infrequently. This poses a challenge for traditional supervised classification algorithms, because there may be very little training data for falls (or none at all) to build generalizable models for falls. This paper proposes an approach for the identification of falls using a wearable device in the absence of training data for falls but with plentiful data for normal ADL. We propose three X-Factor Hidden Markov Model (XHMMs) approaches. The XHMMs have inflated output covariances (observation models). To estimate the inflated covariances, we propose a novel cross validation method to remove outliers from the normal ADL that serves as proxies for the unseen falls and allow learning the XHMMs using only normal activities. We tested the proposed XHMM approaches on two activity recognition datasets and show high detection rates for falls in the absence of fall-specific training data. We show that the traditional method of choosing threshold based on maximum of negative of log-likelihood to identify unseen falls is ill-posed for this problem. We also show that supervised classification methods perform poorly when very limited fall data is available during the training phase.

Connected through hyperedge HE-1 (induced by source paper "deepfall non invasive fall detection with deep spatio temporal convolutional autoencoders"):
  with members:
    - "robust deep and inductive anomaly detection"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "detecting unseen falls from wearable devices using channel wise ensemble of autoencoders"
    - "learning temporal regularity in video sequences"
    - "one class classification taxonomy of study and review of techniques"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "detecting unseen falls from wearable devices using channel wise ensemble of autoencoders"):
  with members:
    - "review of fall detection techniques a data availability perspective"
    - "modular autoencoders for ensemble feature extraction"
    - "falls as anomalies an experimental evaluation using smartphone accelerometer data"
    - "one class classification taxonomy of study and review of techniques"

Connected through hyperedge HE-3 (induced by source paper "unimib shar a new dataset for human activity recognition using acceleration data from smartphones"):
  with members:
    - "falls as anomalies an experimental evaluation using smartphone accelerometer data"

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)