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: "binge watching scaling affordance learning from sitcoms"
  Abstract: In recent years, there has been a renewed interest in jointly modeling perception and action. At the core of this investigation is the idea of modeling affordances(Affordances are opportunities of interaction in the scene. In other words, it represents what actions can the object be used for). However, when it comes to predicting affordances, even the state of the art approaches still do not use any ConvNets. Why is that? Unlike semantic or 3D tasks, there still does not exist any large-scale dataset for affordances. In this paper, we tackle the challenge of creating one of the biggest dataset for learning affordances. We use seven sitcoms to extract a diverse set of scenes and how actors interact with different objects in the scenes. Our dataset consists of more than 10K scenes and 28K ways humans can interact with these 10K images. We also propose a two-step approach to predict affordances in a new scene. In the first step, given a location in the scene we classify which of the 30 pose classes is the likely affordance pose. Given the pose class and the scene, we then use a Variational Autoencoder (VAE) to extract the scale and deformation of the pose. The VAE allows us to sample the distribution of possible poses at test time. Finally, we show the importance of large-scale data in learning a generalizable and robust model of affordances.

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"
    - "charades ego a large scale dataset of paired third and first person videos"

Connected through hyperedge HE-2 (induced by source paper "multimodal image outpainting with regularized normalized diversification"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "object driven text to image synthesis via adversarial training"
    - "normalized diversification"
    - "putting humans in a scene learning affordance in 3d indoor environments"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"

Connected through hyperedge HE-3 (induced by source paper "generating 3d people in scenes without people"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning elementary structures for 3d shape generation and matching"
    - "does computer vision matter for action"
    - "resolving 3d human pose ambiguities with 3d scene constraints"
    - "the replica dataset a digital replica of indoor spaces"
    - "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"
    - "expressive body capture 3d hands face and body from a single image"
    - "imapper interaction guided scene mapping from monocular videos"

Connected through hyperedge HE-4 (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"
    - "scaling egocentric vision the epic kitchens dataset"
    - "human action recognition and prediction a survey"
    - "grounded human object interaction hotspots from video"

Connected through hyperedge HE-5 (induced by source paper "learning to sit synthesizing human chair interactions via hierarchical control"):
  with members:
    - "convolutional sequence to sequence model for human dynamics"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "sfv reinforcement learning of physical skills from videos"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "learning human motion models for long term predictions"
    - "deep representation learning for human motion prediction and classification"

Connected through hyperedge HE-6 (induced by source paper "geometric pose affordance 3d human pose with scene constraints"):
  with members:
    - "imapper interaction guided scene mapping from monocular videos"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "putting humans in a scene learning affordance in 3d indoor environments"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "simple baselines for human pose estimation and tracking"
    - "3d human pose estimation in the wild by adversarial learning"
    - "human motion capture using a drone"

Connected through hyperedge HE-7 (induced by source paper "putting humans in a scene learning affordance in 3d indoor environments"):
  with members:
    - "neural inverse rendering of an indoor scene from a single image"
    - "virtualhome simulating household activities via programs"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "context aware synthesis and placement of object instances"
    - "unified perceptual parsing for scene understanding"
    - "numerical coordinate regression with convolutional neural networks"
    - "learning to act properly predicting and explaining affordances from images"

Connected through hyperedge HE-8 (induced by source paper "grounded human object interaction hotspots from video"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "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"
    - "learning to generate long term future via hierarchical prediction"

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)