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: "mediapipe a framework for building perception pipelines"
  Abstract: Building applications that perceive the world around them is challenging. A developer needs to (a) select and develop corresponding machine learning algorithms and models, (b) build a series of prototypes and demos, (c) balance resource consumption against the quality of the solutions, and finally (d) identify and mitigate problematic cases. The MediaPipe framework addresses all of these challenges. A developer can use MediaPipe to build prototypes by combining existing perception components, to advance them to polished cross-platform applications and measure system performance and resource consumption on target platforms. We show that these features enable a developer to focus on the algorithm or model development and use MediaPipe as an environment for iteratively improving their application with results reproducible across different devices and platforms. MediaPipe will be open-sourced at this https URL.

Connected through hyperedge HE-1 (induced by source paper "mobilepose real time pose estimation for unseen objects with weak shape supervision"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "hybridpose 6d object pose estimation under hybrid representations"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "object as distribution"
    - "centernet keypoint triplets for object detection"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "objects as points"

Connected through hyperedge HE-2 (induced by source paper "adversarially robust frame sampling with bounded irregularities"):
  with members:
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "ensemblenet end to end optimization of multi headed models"
    - "attacking automatic video analysis algorithms a case study of google cloud video intelligence api"
    - "youtube 8m a large scale video classification benchmark"
    - "deep residual learning for image recognition"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "designing the next generation of intelligent personal robotic assistants for the physically impaired"):
  with members:
    - "streaming end to end speech recognition for mobile devices"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "streaming small footprint keyword spotting using sequence to sequence models"
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"
    - "wavenet a generative model for raw audio"
    - "facenet a unified embedding for face recognition and clustering"

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.DC(Distributed, Parallel, and Cluster Computing)