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 models for actions and person object interactions with transfer to question answering"
  Abstract: This paper proposes deep convolutional network models that utilize local and global context to make human activity label predictions in still images, achieving state-of-the-art performance on two recent datasets with hundreds of labels each. We use multiple instance learning to handle the lack of supervision on the level of individual person instances, and weighted loss to handle unbalanced training data. Further, we show how specialized features trained on these datasets can be used to improve accuracy on the Visual Question Answering (VQA) task, in the form of multiple choice fill-in-the-blank questions (Visual Madlibs). Specifically, we tackle two types of questions on person activity and person-object relationship and show improvements over generic features trained on the ImageNet classification task.

Connected through hyperedge HE-1 (induced by source paper "gid net detecting human object interaction with global and instance dependency"):
  with members:
    - "pose aware multi level feature network for human object interaction detection"
    - "large scale visual relationship understanding"
    - "learning human object interactions by graph parsing neural networks"
    - "dual attention network for scene segmentation"
    - "detecting and recognizing human object interactions"
    - "non local neural networks"
    - "learning to detect human object interactions"
    - "attentional pooling for action recognition"

Connected through hyperedge HE-2 (induced by source paper "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "learning human object interactions by graph parsing neural networks"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-3 (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"
    - "binge watching scaling affordance learning from sitcoms"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "turbo learning framework for human object interactions recognition and human pose estimation"):
  with members:
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "detecting and recognizing human object interactions"
    - "human pose regression by combining indirect part detection and contextual information"
    - "learning to detect human object interactions"
    - "knowledge guided deep fractal neural networks for human pose estimation"
    - "mask r cnn"
    - "convolutional pose machines"
    - "rmpe regional multi person pose estimation"

Connected through hyperedge HE-5 (induced by source paper "hake human activity knowledge engine"):
  with members:
    - "ican instance centric attention network for human object interaction detection"
    - "pairwise body part attention for recognizing human object interactions"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "learning to detect human object interactions"
    - "care about you towards large scale human centric visual relationship detection"
    - "attentional pooling for action recognition"

Connected through hyperedge HE-6 (induced by source paper "straight to the facts learning knowledge base retrieval for factual visual question answering"):
  with members:
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "learning to reason end to end module networks for visual question answering"
    - "creativity generating diverse questions using variational autoencoders"
    - "high order attention models for visual question answering"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "multimodal residual learning for visual qa"

Connected through hyperedge HE-7 (induced by source paper "interaction aware spatio temporal pyramid attention networks for action classification"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "attend and interact higher order object interactions for video understanding"
    - "attention is all you need"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "attention clusters purely attention based local feature integration for video classification"
    - "attentional pooling for action recognition"

Connected through hyperedge HE-8 (induced by source paper "learning human object interactions by graph parsing neural networks"):
  with members:
    - "situation recognition with graph neural networks"
    - "detecting and recognizing human object interactions"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "predicting human activities using stochastic grammar"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "deformable convolutional networks"
    - "scene graph generation by iterative message passing"
    - "neural message passing for quantum chemistry"

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)