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: "lsta long short term attention for egocentric action recognition"
  Abstract: Egocentric activity recognition is one of the most challenging tasks in video analysis. It requires a fine-grained discrimination of small objects and their manipulation. While some methods base on strong supervision and attention mechanisms, they are either annotation consuming or do not take spatio-temporal patterns into account. In this paper we propose LSTA as a mechanism to focus on features from spatial relevant parts while attention is being tracked smoothly across the video sequence. We demonstrate the effectiveness of LSTA on egocentric activity recognition with an end-to-end trainable two-stream architecture, achieving state of the art performance on four standard benchmarks.

Connected through hyperedge HE-1 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition"):
  with members:
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "baidu uts submission to the epic kitchens action recognition challenge 2019"
    - "an evaluation of action recognition models on epic kitchens"
    - "large scale weakly supervised pre training for video action recognition"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "actor centric relation network"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-2 (induced by source paper "joint encoding of appearance and motion features with self supervision for first person action recognition"):
  with members:
    - "grasp2vec learning object representations from self supervised grasping"
    - "boosting self supervised learning via knowledge transfer"
    - "unsupervised representation learning by predicting image rotations"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "audio visual scene analysis with self supervised multisensory features"
    - "representation learning by learning to count"
    - "online action detection"
    - "learning features by watching objects move"

Connected through hyperedge HE-3 (induced by source paper "gate shift networks for video action recognition"):
  with members:
    - "attentive spatio temporal representation learning for diving classification"
    - "video modeling with correlation networks"
    - "action recognition with spatial temporal discriminative filter banks"
    - "stm spatiotemporal and motion encoding for action recognition"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "grouped spatial temporal aggregation for efficient action recognition"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"

Connected through hyperedge HE-4 (induced by source paper "multitask learning to improve egocentric action recognition"):
  with members:
    - "mutual context network for jointly estimating egocentric gaze and actions"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "egocentric hand track and object based human action recognition"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "seeing and hearing egocentric actions how much can we learn"):
  with members:
    - "fbk hupba submission to the epic kitchens 2019 action recognition challenge"
    - "uncertainty aware audiovisual activity recognition using deep bayesian variational inference"
    - "baidu uts submission to the epic kitchens action recognition challenge 2019"
    - "audio based activities of daily living adl recognition with large scale acoustic embeddings from online videos"
    - "how much does audio matter to recognize egocentric object interactions"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "object level visual reasoning in videos"

Connected through hyperedge HE-7 (induced by source paper "baidu uts submission to the epic kitchens action recognition challenge 2019"):
  with members:
    - "faster recurrent networks for efficient video classification"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "object level visual reasoning in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "learnable pooling with context gating for video classification"
    - "actionvlad learning spatio temporal aggregation for action classification"

Connected through hyperedge HE-8 (induced by source paper "an analysis of deep neural networks with attention for action recognition from a neurophysiological perspective"):
  with members:
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "top down attention recurrent vlad encoding for action recognition in videos"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep networks can resemble human feed forward vision in invariant object recognition"
    - "deep residual learning for image recognition"

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)