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: "robojam a musical mixture density network for collaborative touchscreen interaction"
  Abstract: RoboJam is a machine-learning system for generating music that assists users of a touchscreen music app by performing responses to their short improvisations. This system uses a recurrent artificial neural network to generate sequences of touchscreen interactions and absolute timings, rather than high-level musical notes. To accomplish this, RoboJam's network uses a mixture density layer to predict appropriate touch interaction locations in space and time. In this paper, we describe the design and implementation of RoboJam's network and how it has been integrated into a touchscreen music app. A preliminary evaluation analyses the system in terms of training, musical generation and user interaction.

Connected through hyperedge HE-1 (induced by source paper "how do mixture density rnns predict the future"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "recurrent world models facilitate policy evolution"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "direct manipulation visualization of deep networks"
    - "a neural representation of sketch drawings"
    - "unsupervised learning for physical interaction through video prediction"
    - "recurrent mixture density network for spatiotemporal visual attention"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-2 (induced by source paper "an interactive musical prediction system with mixture density recurrent neural networks"):
  with members:
    - "deep predictive models in interactive music"
    - "a neural representation of sketch drawings"
    - "sequence tutor conservative fine tuning of sequence generation models with kl control"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "data driven analysis of tiny touchscreen performance with microjam"):
  with members:
    - "how constraints affect content the case of twitter s switch from 140 to 280 characters"
    - "6 seconds of sound and vision creativity in micro videos"

Connected through hyperedge HE-4 (induced by source paper "deep predictive models in interactive music"):
  with members:
    - "a hierarchical latent vector model for learning long term structure in music"
    - "neural translation of musical style"
    - "generating sequences with recurrent neural 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.HC(Human-Computer Interaction)