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: "when will you do what anticipating temporal occurrences of activities"
  Abstract: Analyzing human actions in videos has gained increased attention recently. While most works focus on classifying and labeling observed video frames or anticipating the very recent future, making long-term predictions over more than just a few seconds is a task with many practical applications that has not yet been addressed. In this paper, we propose two methods to predict a considerably large amount of future actions and their durations. Both, a CNN and an RNN are trained to learn future video labels based on previously seen content. We show that our methods generate accurate predictions of the future even for long videos with a huge amount of different actions and can even deal with noisy or erroneous input information.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "is the pedestrian going to cross answering by 2d pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "scene graph generation via conditional random fields"

Connected through hyperedge HE-2 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks"):
  with members:
    - "marginalized average attentional network for weakly supervised learning"
    - "dynamic graph modules for modeling higher order interactions in activity recognition"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "w talc weakly supervised temporal activity localization and classification"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph modules for modeling object object interactions in activity recognition"
    - "a structured model for action detection"

Connected through hyperedge HE-3 (induced by source paper "learning video representations using contrastive bidirectional transformer"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "lxmert learning cross modality encoder representations from transformers"
    - "contrastive multiview coding"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "learning correspondence from the cycle consistency of time"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "data efficient image recognition with contrastive predictive coding"
    - "bert has a mouth and it must speak bert as a markov random field language model"

Connected through hyperedge HE-4 (induced by source paper "procedure planning in instructional videos"):
  with members:
    - "cross task weakly supervised learning from instructional videos"
    - "videobert a joint model for video and language representation learning"
    - "coin a large scale dataset for comprehensive instructional video analysis"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-5 (induced by source paper "captioning near future activity sequences"):
  with members:
    - "move forward and tell a progressive generator of video descriptions"
    - "dense captioning events in videos"
    - "estimating human intent for physical human robot co manipulation"
    - "towards automatic learning of procedures from web instructional videos"
    - "recurrent topic transition gan for visual paragraph generation"
    - "spice semantic propositional image caption evaluation"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "recognizing fine grained and composite activities using hand centric features and script data"

Connected through hyperedge HE-6 (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"
    - "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"
    - "visual forecasting by imitating dynamics in natural sequences"

Connected through hyperedge HE-7 (induced by source paper "learning representations for predicting future activities"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "stochastic adversarial video prediction"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "predicting action tubes"
    - "predicting deeper into the future of semantic segmentation"
    - "on calibration of modern neural networks"
    - "contextvp fully context aware video prediction"

Connected through hyperedge HE-8 (induced by source paper "a variational auto encoder model for stochastic point processes"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"
    - "time perception machine temporal point processes for the when where and what of activity prediction"
    - "disentangled sequential autoencoder"
    - "classify predict detect anticipate and synthesize hierarchical recurrent latent variable models for human activity modeling"
    - "a probabilistic semi supervised approach to multi task human activity modeling"
    - "toward controlled generation of text"

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)