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: "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
  Abstract: Anticipating the future actions of a human is a widely studied problem in robotics that requires spatio-temporal reasoning. In this work we propose a deep learning approach for anticipation in sensory-rich robotics applications. We introduce a sensory-fusion architecture which jointly learns to anticipate and fuse information from multiple sensory streams. Our architecture consists of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory (LSTM) units to capture long temporal dependencies. We train our architecture in a sequence-to-sequence prediction manner, and it explicitly learns to predict the future given only a partial temporal context. We further introduce a novel loss layer for anticipation which prevents over-fitting and encourages early anticipation. We use our architecture to anticipate driving maneuvers several seconds before they happen on a natural driving data set of 1180 miles. The context for maneuver anticipation comes from multiple sensors installed on the vehicle. Our approach shows significant improvement over the state-of-the-art in maneuver anticipation by increasing the precision from 77.4% to 90.5% and recall from 71.2% to 87.4%.

Connected through hyperedge HE-1 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "learning to drive in a day"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"

Connected through hyperedge HE-2 (induced by source paper "future state predicting lstm for early surgery type recognition"):
  with members:
    - "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "learning to generate long term future via hierarchical prediction"
    - "encouraging lstms to anticipate actions very early"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "temporal segment networks towards good practices for deep action recognition"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-3 (induced by source paper "predicting the future a jointly learnt model for action anticipation"):
  with members:
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "deep cosine metric learning for person re identification"
    - "convolutional sequence to sequence model for human dynamics"
    - "multi level sequence gan for group activity recognition"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "action anticipation by predicting future dynamic images"
    - "encouraging lstms to anticipate actions very early"

Connected through hyperedge HE-4 (induced by source paper "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"):
  with members:
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "when will you do what anticipating temporal occurrences of activities"
    - "lsta long short term attention for egocentric action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "forecasting hand and object locations in future frames"
    - "am i done predicting action progress in videos"
    - "encouraging lstms to anticipate actions very early"

Connected through hyperedge HE-5 (induced by source paper "temporal multimodal fusion for driver behavior prediction tasks using gated recurrent fusion units"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "learning to drive in a day"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "diverse trajectory forecasting with determinantal point processes"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "learning 3d human dynamics from video"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "learning detection with diverse proposals"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "creating capsule wardrobes from fashion images"
    - "deep representation learning for human motion prediction and classification"

Connected through hyperedge HE-7 (induced by source paper "forecasting future sequence of actions to complete an activity"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "forecasting future action sequences with neural memory networks"
    - "action anticipation by predicting future dynamic images"
    - "when will you do what anticipating temporal occurrences of activities"
    - "agent centric risk assessment accident anticipation and risky region localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "predicting human activities using stochastic grammar"
    - "encouraging lstms to anticipate actions very early"

Connected through hyperedge HE-8 (induced by source paper "action anticipation with rbf kernelized feature mapping rnn"):
  with members:
    - "mocogan decomposing motion and content for video generation"
    - "encouraging lstms to anticipate actions very early"
    - "red reinforced encoder decoder networks for action anticipation"
    - "the pose knows video forecasting by generating pose futures"
    - "temporal generative adversarial nets with singular value clipping"
    - "densely connected convolutional networks"
    - "online real time multiple spatiotemporal action localisation and prediction"
    - "online localization and prediction of actions and interactions"

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)