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: "leveraging structural context models and ranking score fusion for human interaction prediction"
  Abstract: Predicting an interaction before it is fully executed is very important in applications, such as human-robot interaction and video surveillance. In a two-human interaction scenario, there are often contextual dependency structures between the global interaction context of the two humans and the local context of the different body parts of each human. In this paper, we propose to learn the structure of the interaction contexts and combine it with the spatial and temporal information of a video sequence to better predict the interaction class. The structural models, including the spatial and the temporal models, are learned with long short term memory (LSTM) networks to capture the dependency of the global and local contexts of each RGB frame and each optical flow image, respectively. LSTM networks are also capable of detecting the key information from global and local interaction contexts. Moreover, to effectively combine the structural models with the spatial and temporal models for interaction prediction, a ranking score fusion method is introduced to automatically compute the optimal weight of each model for score fusion. Experimental results on the BIT-Interaction Dataset and the UT-Interaction Dataset clearly demonstrate the benefits of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "skeleton based online action prediction using scale selection network"):
  with members:
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "skeleton boxes solving skeleton based action detection with a single deep convolutional neural network"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cascaded boundary regression for temporal action detection"
    - "temporal context network for activity localization in videos"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-2 (induced by source paper "interaction relational network for mutual action recognition"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "hierarchical long short term concurrent memory for human interaction recognition"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "3d human pose estimation with relational networks"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "a simple neural network module for relational reasoning"
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"

Connected through hyperedge HE-3 (induced by source paper "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "generic probabilistic interactive situation recognition and prediction from virtual to real"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "the numerics of gans"
    - "imitating driver behavior with generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "global temporal representation based cnns for infrared action recognition"):
  with members:
    - "transferable feature representation for visible to infrared cross dataset human action recognition"
    - "learning spatiotemporal features for infrared action recognition with 3d convolutional neural networks"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "convolutional two stream network fusion for video action recognition"
    - "long term temporal convolutions for action recognition"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "skeleton based human action recognition with global context aware attention lstm networks"):
  with members:
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "online human action detection using joint classification regression recurrent neural networks"
    - "deep multimodal feature analysis for action recognition in rgb d videos"

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)