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: "self supervised adaptation of high fidelity face models for monocular performance tracking"
  Abstract: Improvements in data-capture and face modeling techniques have enabled us to create high-fidelity realistic face models. However, driving these realistic face models requires special input data, e.g. 3D meshes and unwrapped textures. Also, these face models expect clean input data taken under controlled lab environments, which is very different from data collected in the wild. All these constraints make it challenging to use the high-fidelity models in tracking for commodity cameras. In this paper, we propose a self-supervised domain adaptation approach to enable the animation of high-fidelity face models from a commodity camera. Our approach first circumvents the requirement for special input data by training a new network that can directly drive a face model just from a single 2D image. Then, we overcome the domain mismatch between lab and uncontrolled environments by performing self-supervised domain adaptation based on "consecutive frame texture consistency" based on the assumption that the appearance of the face is consistent over consecutive frames, avoiding the necessity of modeling the new environment such as lighting or background. Experiments show that we are able to drive a high-fidelity face model to perform complex facial motion from a cellphone camera without requiring any labeled data from the new domain.

Connected through hyperedge HE-1 (induced by source paper "a baseline for 3d multi object tracking"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "how to train your deep multi object tracker"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "deepmot a differentiable framework for training multiple object trackers"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "fantrack 3d multi object tracking with feature association network"
    - "future near collision prediction from monocular video feasibility dataset and challenges"
    - "mots multi object tracking and segmentation"

Connected through hyperedge HE-2 (induced by source paper "humbi a large multiview dataset of human body expressions"):
  with members:
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "learning joint reconstruction of hands and manipulated objects"
    - "towards social artificial intelligence nonverbal social signal prediction in a triadic interaction"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "deep appearance models for face rendering"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning to estimate 3d hand pose from single rgb images"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

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.CV(Computer Vision and Pattern Recognition)