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: "learning action maps of large environments via first person vision"
  Abstract: When people observe and interact with physical spaces, they are able to associate functionality to regions in the environment. Our goal is to automate dense functional understanding of large spaces by leveraging sparse activity demonstrations recorded from an ego-centric viewpoint. The method we describe enables functionality estimation in large scenes where people have behaved, as well as novel scenes where no behaviors are observed. Our method learns and predicts "Action Maps", which encode the ability for a user to perform activities at various locations. With the usage of an egocentric camera to observe human activities, our method scales with the size of the scene without the need for mounting multiple static surveillance cameras and is well-suited to the task of observing activities up-close. We demonstrate that by capturing appearance-based attributes of the environment and associating these attributes with activity demonstrations, our proposed mathematical framework allows for the prediction of Action Maps in new environments. Additionally, we offer a preliminary glance of the applicability of Action Maps by demonstrating a proof-of-concept application in which they are used in concert with activity detections to perform localization.

Connected through hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "videograph recognizing minutes long human activities in videos"
    - "anticipation and next action forecasting in video an end to end model with memory"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "binge watching scaling affordance learning from sitcoms"

Connected through hyperedge HE-2 (induced by source paper "forecasting human object interaction joint prediction of motor attention and egocentric activity"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "structured prediction helps 3d human motion modelling"
    - "paying more attention to motion attention distillation for learning video representations"
    - "deeply supervised salient object detection with short connections"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "binge watching scaling affordance learning from sitcoms"
    - "scaling egocentric vision the epic kitchens dataset"
    - "human action recognition and prediction a survey"

Connected through hyperedge HE-3 (induced by source paper "grounded human object interaction hotspots from video"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "binge watching scaling affordance learning from sitcoms"
    - "scaling egocentric vision the epic kitchens dataset"
    - "visual affordance and function understanding a survey"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "joint discovery of object states and manipulation actions"
    - "dual motion gan for future flow embedded video prediction"
    - "self supervised visual planning with temporal skip connections"

Connected through hyperedge HE-4 (induced by source paper "visual affordance and function understanding a survey"):
  with members:
    - "binge watching scaling affordance learning from sitcoms"
    - "geometric affordances from a single example via the interaction tensor"
    - "predicting human activities using stochastic grammar"
    - "learning social affordance grammar from videos transferring human interactions to human robot interactions"
    - "deep affordance grounded sensorimotor object recognition"
    - "transfer of tool affordance and manipulation cues with 3d vision data"
    - "adaptive binarization for weakly supervised affordance segmentation"
    - "learning to act properly predicting and explaining affordances from images"

Connected through hyperedge HE-5 (induced by source paper "actor and observer joint modeling of first and third person videos"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "multimodal machine learning a survey and taxonomy"
    - "identifying first person camera wearers in third person videos"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "ego surfing first person videos"
    - "learning features by watching objects move"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "the open world of micro videos"

Connected through hyperedge HE-6 (induced by source paper "from lifestyle vlogs to everyday interactions"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "dilated residual networks"
    - "the kinetics human action video dataset"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "the something something video database for learning and evaluating visual common sense"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "densely connected convolutional networks"
    - "an uncertain future forecasting from static images using variational autoencoders"

Connected through hyperedge HE-7 (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"
    - "general automatic human shape and motion capture using volumetric contour cues"

Connected through hyperedge HE-8 (induced by source paper "first person activity forecasting with online inverse reinforcement learning"):
  with members:
    - "social behavior prediction from first person videos"
    - "a game theoretic approach to multi pedestrian activity forecasting"
    - "going deeper into first person activity recognition"
    - "learning to filter with predictive state inference machines"
    - "you only look once unified real time object detection"
    - "anticipating visual representations from unlabeled video"
    - "orb slam a versatile and accurate monocular slam system"
    - "very deep convolutional networks for large scale image recognition"

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)