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: "subjects and their objects localizing interactees for a person centric view of importance"
  Abstract: Understanding images with people often entails understanding their \emph{interactions} with other objects or people. As such, given a novel image, a vision system ought to infer which other objects/people play an important role in a given person's activity. However, existing methods are limited to learning action-specific interactions (e.g., how the pose of a tennis player relates to the position of his racquet when serving the ball) for improved recognition, making them unequipped to reason about novel interactions with actions or objects unobserved in the training data. #R##N#We propose to predict the "interactee" in novel images---that is, to localize the \emph{object} of a person's action. Given an arbitrary image with a detected person, the goal is to produce a saliency map indicating the most likely positions and scales where that person's interactee would be found. To that end, we explore ways to learn the generic, action-independent connections between (a) representations of a person's pose, gaze, and scene cues and (b) the interactee object's position and scale. We provide results on a newly collected UT Interactee dataset spanning more than 10,000 images from SUN, PASCAL, and COCO. We show that the proposed interaction-informed saliency metric has practical utility for four tasks: contextual object detection, image retargeting, predicting object importance, and data-driven natural language scene description. All four scenarios reveal the value in linking the subject to its object in order to understand the story of an image.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"):
  with members:
    - "detecting gaze towards eyes in natural social interactions and its use in child assessment"
    - "eye tracking for everyone"
    - "it s written all over your face full face appearance based gaze estimation"
    - "visual saliency based on multiscale deep features"
    - "deep residual learning for image recognition"
    - "salient object detection a benchmark"
    - "appearance based gaze estimation in the wild"
    - "rendering of eyes for eye shape registration and gaze estimation"

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)