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: "towards the design of effective freehand gestural interaction for interactive tv"
  Abstract: As interactive devices become pervasive, people are beginning to looking for more advanced interaction with televisions in the living room. Interactive television has the potential to offer a very engaging experience. But most common user tasks are still challenging with such systems, such as menu selection or text input. And little work has been done on understanding and sup-porting the effective design of freehand interaction with an TV in the living room. In this paper, we perform two studies investi-gating freehand gestural interaction with a consumer level sensor, which is suitable for TV scenarios. In the first study, we inves-tigate a range of design factors for tiled layout menu selection, including wearable feedback, push gesture depth, target size and position in motor space. The results show that tactile and audio feedback have no significant effect on performance and prefer-ence, and these results inform potential designs for high selection performance. In the second study, we investigate a common TV user task of text input using freehand gesture. We design and evaluate two virtual keyboard layouts and three freehand selec-tion methods. Results show that ease of use and error tolerance can be both achieved using a text entry method utilizing a dual circle layout and an expanding target selection technique. Finally, we propose design guidelines for effective, usable and com-fortable freehand gestural interaction for interactive TV based on the findings.

Connected through hyperedge HE-1 (induced by source paper "automatic structural scene digitalization"):
  with members:
    - "blur robust optical flow using motion channel"
    - "video interpolation using optical flow and laplacian smoothness"
    - "drift robust non rigid optical flow enhancement for long sequences"
    - "dense motion estimation for smoke"

Connected through hyperedge HE-2 (induced by source paper "learn to model motion from blurry footages"):
  with members:
    - "learning to extract motion from videos in convolutional neural networks"
    - "dense motion estimation for smoke"
    - "learning to deblur"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "nonrigid optical flow ground truth for real world scenes with time varying shading effects"):
  with members:
    - "video interpolation using optical flow and laplacian smoothness"
    - "drift robust non rigid optical flow enhancement for long sequences"

Connected through hyperedge HE-4 (induced by source paper "video interpolation using optical flow and laplacian smoothness"):
  with members:
    - "nonrigid optical flow ground truth for real world scenes with time varying shading effects"
    - "blur robust optical flow using motion channel"
    - "drift robust non rigid optical flow enhancement for long sequences"
    - "dense motion estimation for smoke"
    - "touch less interactive augmented reality game on vision based wearable device"

Connected through hyperedge HE-5 (induced by source paper "drift robust non rigid optical flow enhancement for long sequences"):
  with members:
    - "blur robust optical flow using motion channel"
    - "video interpolation using optical flow and laplacian smoothness"

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)