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: "recognizing activities of daily living with a wrist mounted camera"
  Abstract: We present a novel dataset and a novel algorithm for recognizing activities of daily living (ADL) from a first-person wearable camera. Handled objects are crucially important for egocentric ADL recognition. For specific examination of objects related to users' actions separately from other objects in an environment, many previous works have addressed the detection of handled objects in images captured from head-mounted and chest-mounted cameras. Nevertheless, detecting handled objects is not always easy because they tend to appear small in images. They can be occluded by a user's body. As described herein, we mount a camera on a user's wrist. A wrist-mounted camera can capture handled objects at a large scale, and thus it enables us to skip object detection process. To compare a wrist-mounted camera and a head-mounted camera, we also develop a novel and publicly available dataset that includes videos and annotations of daily activities captured simultaneously by both cameras. Additionally, we propose a discriminative video representation that retains spatial and temporal information after encoding frame descriptors extracted by Convolutional Neural Networks (CNN).

Connected through hyperedge HE-1 (induced by source paper "mo2cap2 real time mobile 3d motion capture with a cap mounted fisheye camera"):
  with members:
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "learning from synthetic humans"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "generating multiple diverse hypotheses for human 3d pose consistent with 2d joint detections"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "anticipating daily intention using on wrist motion triggered sensing"):
  with members:
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "going deeper into first person activity recognition"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "title generation for user generated videos"
    - "jointly modeling embedding and translation to bridge video and language"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "car that knows before you do anticipating maneuvers via learning temporal driving models"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"

Connected through hyperedge HE-3 (induced by source paper "egocap egocentric marker less motion capture with two fisheye cameras"):
  with members:
    - "a versatile scene model with differentiable visibility applied to generative pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "detecting engagement in egocentric video"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "going deeper into first person activity recognition"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "learning action maps of large environments via first person vision"

Connected through hyperedge HE-4 (induced by source paper "the color of the cat is gray 1 million full sentences visual question answering fsvqa"):
  with members:
    - "training recurrent answering units with joint loss minimization for vqa"
    - "dualnet domain invariant network for visual question answering"
    - "multimodal residual learning for visual qa"
    - "hierarchical question image co attention for visual question answering"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "exploring models and data for image question answering"
    - "vqa visual question answering"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-5 (induced by source paper "personalized image based user authentication using wearable cameras"):
  with members:
    - "predicting daily activities from egocentric images using deep learning"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"
    - "egocentric video biometrics"

Connected through hyperedge HE-6 (induced by source paper "recognition from hand cameras"):
  with members:
    - "deep image scaling up image recognition"
    - "two stream convolutional networks for action recognition in videos"

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)