human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "eeg to f0 establishing artificial neuro muscular pathway for kinematics based fundamental frequency control"):
  Abstract: The fundamental frequency (F0) of human voice is generally controlled by changing the vocal fold parameters (including tension, length and mass), which in turn is manipulated by the muscle exciters, activated by the neural synergies. In order to begin investigating the neuromuscular F0 control pathway, we simulate a simple biomechanical arm prototype (instead of an artificial vocal tract) that tends to control F0 of an artificial sound synthesiser based on the elbow movements. The intended arm movements are decoded from the EEG signal inputs (collected simultaneously with the kinematic hand data of the participant) through a combined machine learning and biomechanical modeling strategy. The machine learning model is employed to identify the muscle activation of a single-muscle arm model in ArtiSynth (from input brain signal), in order to match the actual kinematic (elbow joint angle) data . The biomechanical model utilises this estimated muscle excitation to produce corresponding changes in elbow angle, which is then linearly mapped to F0 of a vocal sound synthesiser. We use the F0 value mapped from the actual kinematic hand data (via same function) as the ground truth and compare the F0 estimated from brain signal. A detailed qualitative and quantitative performance comparison shows that the proposed neuromuscular pathway can indeed be utilised to accurately control the vocal fundamental frequency, thereby demonstrating the success of our closed loop neuro-biomechanical control scheme.

Includes members:
  Member "speak your mind towards imagined speech recognition with hierarchical deep learning":
    also connected through other hyperedges:
      (none)
  Member "deep learning the eeg manifold for phonological categorization from active thoughts":
    also connected through other hyperedges:
      (none)
  Member "hierarchical deep feature learning for decoding imagined speech from eeg":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "touchless typing using head movement based gestures")
  Member "sound stream ii towards real time gesture controlled articulatory sound synthesis":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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.HC(Human-Computer Interaction)