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: "harnessing ai for speech reconstruction using multi view silent video feed"
  Abstract: Speechreading or lipreading is the technique of understanding and getting phonetic features from a speaker's visual features such as movement of lips, face, teeth and tongue. It has a wide range of multimedia applications such as in surveillance, Internet telephony, and as an aid to a person with hearing impairments. However, most of the work in speechreading has been limited to text generation from silent videos. Recently, research has started venturing into generating (audio) speech from silent video sequences but there have been no developments thus far in dealing with divergent views and poses of a speaker. Thus although, we have multiple camera feeds for the speech of a user, but we have failed in using these multiple video feeds for dealing with the different poses. To this end, this paper presents the world's first ever multi-view speech reading and reconstruction system. This work encompasses the boundaries of multimedia research by putting forth a model which leverages silent video feeds from multiple cameras recording the same subject to generate intelligent speech for a speaker. Initial results confirm the usefulness of exploiting multiple camera views in building an efficient speech reading and reconstruction system. It further shows the optimal placement of cameras which would lead to the maximum intelligibility of speech. Next, it lays out various innovative applications for the proposed system focusing on its potential prodigious impact in not just security arena but in many other multimedia analytics problems.

Connected through hyperedge HE-1 (induced by source paper "harnessing gans for zero shot learning of new classes in visual speech recognition"):
  with members:
    - "modality attention for end to end audio visual speech recognition"
    - "low shot learning from imaginary data"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "end to end multi view lipreading"
    - "lipreading with long short term memory"
    - "matching networks for one shot learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "mobivsr a visual speech recognition solution for mobile devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "model compression via distillation and quantization"
    - "learning motion in feature space locally consistent deformable convolution networks for fine grained action detection"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "dynamically hierarchy revolution dirnet for compressing recurrent neural network on mobile devices"
    - "3d depthwise convolution reducing model parameters in 3d vision tasks"
    - "improved speech reconstruction from silent video"

Connected through hyperedge HE-3 (induced by source paper "harnessing gans for addition of new classes in vsr"):
  with members:
    - "generative adversarial networks an overview"
    - "modality attention for end to end audio visual speech recognition"
    - "low shot learning from imaginary data"
    - "cold start aware user and product attention for sentiment classification"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "lip2audspec speech reconstruction from silent lip movements video"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "end to end multi view lipreading"

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.SD(Sound)