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: "automatic classification of functional gait disorders"
  Abstract: This paper proposes a comprehensive investigation of the automatic classification of functional gait disorders (GDs) based solely on ground reaction force (GRF) measurements. The aim of this study is twofold: first, to investigate the suitability of the state-of-the-art GRF parameterization techniques (representations) for the discrimination of functional GDs; and second, to provide a first performance baseline for the automated classification of functional GDs for a large-scale dataset. The utilized database comprises GRF measurements from 279 patients with GDs and data from 161 healthy controls ( N ). Patients were manually classified into four classes with different functional impairments associated with the “hip”, “knee”, “ankle”, and “calcaneus”. Different parameterizations are investigated: GRF parameters, global principal component analysis (PCA) based representations, and a combined representation applying PCA on GRF parameters. The discriminative power of each parameterization for different classes is investigated by linear discriminant analysis. Based on this analysis, two classification experiments are pursued: distinction between healthy and impaired gait ( N  versus GD) and multiclass classification between healthy gait and all four GD classes. Experiments show promising results and reveal among others that several factors, such as imbalanced class cardinalities and varying numbers of measurement sessions per patient, have a strong impact on the classification accuracy and therefore need to be taken into account. The results represent a promising first step toward the automated classification of GDs and a first performance baseline for future developments in this direction.

Connected through hyperedge HE-1 (induced by source paper "on the understanding and interpretation of machine learning predictions in clinical gait analysis using explainable artificial intelligence"):
  with members:
    - "towards best practice in explaining neural network decisions with lrp"
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining the unique nature of individual gait patterns with deep learning"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "a unified approach to interpreting model predictions"

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)