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: "situation recognition with graph neural networks"
  Abstract: We address the problem of recognizing situations in images. Given an image, the task is to predict the most salient verb (action), and fill its semantic roles such as who is performing the action, what is the source and target of the action, etc. Different verbs have different roles (e.g. attacking has weapon), and each role can take on many possible values (nouns). We propose a model based on Graph Neural Networks that allows us to efficiently capture joint dependencies between roles using neural networks defined on a graph. Experiments with different graph connectivities show that our approach that propagates information between roles significantly outperforms existing work, as well as multiple baselines. We obtain roughly 3-5% improvement over previous work in predicting the full situation. We also provide a thorough qualitative analysis of our model and influence of different roles in the verbs.

Connected through hyperedge HE-1 (induced by source paper "bayesian relational memory for semantic visual navigation"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "composable planning with attributes"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-2 (induced by source paper "human pose estimation with spatial contextual information"):
  with members:
    - "path aggregation network for instance segmentation"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "multi context attention for human pose estimation"
    - "mask r cnn"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-3 (induced by source paper "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "spatio temporal action graph networks"
    - "learning actor relation graphs for group activity recognition"
    - "intent aware multi agent reinforcement learning"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "classifying collisions with spatio temporal action graph networks"
    - "semi supervised learning fusion of self supervised supervised learning and multimodal cues for tactical driver behavior detection"

Connected through hyperedge HE-4 (induced by source paper "spatially aware graph neural networks for relational behavior forecasting from sensor data"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "relational action forecasting"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "context aware human motion prediction"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "bihmp gan bidirectional 3d human motion prediction gan"
    - "an lstm network for highway trajectory prediction"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic graph cnn for learning on point clouds"
    - "videos as space time region graphs"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "structure inference net object detection using scene level context and instance level relationships"

Connected through hyperedge HE-6 (induced by source paper "an attention enhanced graph convolutional lstm network for skeleton based action recognition"):
  with members:
    - "part based graph convolutional network for action recognition"
    - "action recognition with spatio temporal visual attention on skeleton image sequences"
    - "memory attention networks for skeleton based action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "learning human object interactions by graph parsing neural networks"
    - "how powerful are graph neural networks"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "dressing as a whole outfit compatibility learning based on node wise graph neural networks"):
  with members:
    - "session based recommendation with graph neural network"
    - "constructing narrative event evolutionary graph for script event prediction"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "compatibility family learning for item recommendation and generation"
    - "style2vec representation learning for fashion items from style sets"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

Connected through hyperedge HE-8 (induced by source paper "fi gnn modeling feature interactions via graph neural networks for ctr prediction"):
  with members:
    - "session based recommendation with graph neural network"
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "semi supervised compatibility learning across categories for clothing matching"
    - "dressing as a whole outfit compatibility learning based on node wise graph neural networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "graph to sequence learning using gated graph neural networks"

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)