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: "hierarchical hidden markov model in detecting activities of daily living in wearable videos for studies of dementia"
  Abstract: This paper presents a method for indexing activities of daily living in videos acquired from wearable cameras. It addresses the problematic of analyzing the complex multimedia data acquired from wearable devices, which has been recently a growing concern due to the increasing amount of this kind of multimedia data. In the context of dementia diagnosis by doctors, patient activities are recorded in the environment of their home using a lightweight wearable device, to be later visualized by the medical practitioners. The recording mode poses great challenges since the video data consists in a single sequence shot where strong motion and sharp lighting changes often appear. Because of the length of the recordings, tools for an efficient navigation in terms of activities of interest are crucial. Our work introduces a video structuring approach that combines automatic motion based segmentation of the video and activity recognition by a hierarchical two-level Hidden Markov Model. We define a multi-modal description space over visual and audio features, including mid-level features such as motion, location, speech and noise detections. We show their complementarities globally as well as for specific activities. Experiments on real data obtained from the recording of several patients at home show the difficulty of the task and the promising results of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "analysis of the hands in egocentric vision a survey"):
  with members:
    - "an effective and efficient method for detecting hands in egocentric videos for rehabilitation applications"
    - "recurrent u net for resource constrained segmentation"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "domain specific priors and meta learning for low shot first person action recognition"
    - "egocentric hand track and object based human action recognition"
    - "egocentric video a new tool for capturing hand use of individuals with spinal cord injury at home"
    - "scaling egocentric vision the epic kitchens dataset"
    - "analysis of hand segmentation in the wild"

Connected through hyperedge HE-2 (induced by source paper "mouse movement and probabilistic graphical models based e learning activity recognition improvement possibilistic model"):
  with members:
    - "hidden markov model for inferring learner task using mouse movement"
    - "pertinent information retrieval based on possibilistic bayesian network origin and possibilistic perspective"
    - "fuzzy knowledge representation learning and optimization with bayesian analysis in fuzzy semantic networks"

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.MM(Multimedia)