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: "red reinforced encoder decoder networks for action anticipation"
  Abstract: Action anticipation aims to detect an action before it happens. Many real world applications in robotics and surveillance are related to this predictive capability. Current methods address this problem by first anticipating visual representations of future frames and then categorizing the anticipated representations to actions. However, anticipation is based on a single past frame's representation, which ignores the history trend. Besides, it can only anticipate a fixed future time. We propose a Reinforced Encoder-Decoder (RED) network for action anticipation. RED takes multiple history representations as input and learns to anticipate a sequence of future representations. One salient aspect of RED is that a reinforcement module is adopted to provide sequence-level supervision; the reward function is designed to encourage the system to make correct predictions as early as possible. We test RED on TVSeries, THUMOS-14 and TV-Human-Interaction datasets for action anticipation and achieve state-of-the-art performance on all datasets.

Connected through hyperedge HE-1 (induced by source paper "ttpp temporal transformer with progressive prediction for efficient action anticipation"):
  with members:
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "attention aware compositional network for person re identification"
    - "video action transformer network"
    - "temporal recurrent networks for online action detection"
    - "attention driven person re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "encouraging lstms to anticipate actions very early"
    - "online action detection"

Connected through hyperedge HE-2 (induced by source paper "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"):
  with members:
    - "read watch and move reinforcement learning for temporally grounding natural language descriptions in videos"
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "fine grained video categorization with redundancy reduction attention"
    - "motion feature network fixed motion filter for action recognition"
    - "compact generalized non local network"
    - "adaframe adaptive frame selection for fast video recognition"

Connected through hyperedge HE-3 (induced by source paper "skeleton based online action prediction using scale selection network"):
  with members:
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "wslln weakly supervised natural language localization networks"):
  with members:
    - "startnet online detection of action start in untrimmed videos"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "weakly supervised dense event captioning in videos"
    - "temporal recurrent networks for online action detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "c wsl count guided weakly supervised localization"

Connected through hyperedge HE-5 (induced by source paper "startnet online detection of action start in untrimmed videos"):
  with members:
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "temporal recurrent networks for online action detection"
    - "adaframe adaptive frame selection for fast video recognition"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal action detection with structured segment networks"
    - "blockdrop dynamic inference paths in residual networks"

Connected through hyperedge HE-6 (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-7 (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-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"
    - "when will you do what anticipating temporal occurrences of activities"
    - "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"

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)