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: "advances in human action recognition a survey"
  Abstract: Human action recognition has been an important topic in computer vision due to its many applications such as video surveillance, human machine interaction and video retrieval. One core problem behind these applications is automatically recognizing low-level actions and high-level activities of interest. The former is usually the basis for the latter. This survey gives an overview of the most recent advances in human action recognition during the past several years, following a well-formed taxonomy proposed by a previous survey [1]. From this state-of-the-art survey, researchers can view a panorama of progress in this area for future research.

Connected through hyperedge HE-1 (induced by source paper "oops predicting unintentional action in video"):
  with members:
    - "learning video representations using contrastive bidirectional transformer"
    - "self supervised learning for video correspondence flow"
    - "gaze360 physically unconstrained gaze estimation in the wild"
    - "learning correspondence from the cycle consistency of time"
    - "joint task self supervised learning for temporal correspondence"
    - "learning representations for predicting future activities"
    - "revisiting self supervised visual representation learning"
    - "back to the future knowledge distillation for human action anticipation"

Connected through hyperedge HE-2 (induced by source paper "lpat learning to predict adaptive threshold for weakly supervised temporal action localization"):
  with members:
    - "dmc net generating discriminative motion cues for fast compressed video action recognition"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "cascaded pyramid mining network for weakly supervised temporal action localization"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"

Connected through hyperedge HE-3 (induced by source paper "spatiotemporal co attention recurrent neural networks for human skeleton motion prediction"):
  with members:
    - "hierarchical long short term concurrent memory for human interaction recognition"
    - "long term human motion prediction by modeling motion context and enhancing motion dynamic"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "deep representation learning for human motion prediction and classification"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "on human motion prediction using recurrent neural networks"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"

Connected through hyperedge HE-4 (induced by source paper "multi level network for high speed multi person pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "towards accurate multi person pose estimation in the wild"
    - "cascaded pyramid network for multi person pose estimation"
    - "multi context attention for human pose estimation"
    - "mask r cnn"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-5 (induced by source paper "predicting human activities from user generated content"):
  with members:
    - "smooth loss functions for deep top k classification"
    - "event2mind commonsense inference on events intents and reactions"
    - "direct network transfer transfer learning of sentence embeddings for semantic similarity"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "augur mining human behaviors from fiction to power interactive systems"
    - "sort story sorting jumbled images and captions into stories"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "autoloc weakly supervised temporal action localization"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "predictive corrective networks for action detection"
    - "temporal action detection with structured segment networks"
    - "deep self taught learning for weakly supervised object localization"
    - "two phase learning for weakly supervised object localization"

Connected through hyperedge HE-7 (induced by source paper "online detection of action start in untrimmed streaming videos"):
  with members:
    - "multi fiber networks for video recognition"
    - "ctap complementary temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "good semi supervised learning that requires a bad gan"
    - "single shot temporal action detection"
    - "predictive corrective networks for action detection"
    - "temporal action detection with structured segment networks"
    - "cascaded boundary regression for temporal action detection"

Connected through hyperedge HE-8 (induced by source paper "unsupervised learning of view invariant action representations"):
  with members:
    - "unsupervised representation learning by predicting image rotations"
    - "transitive invariance for self supervised visual representation learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "unsupervised representation learning by sorting sequences"
    - "representation learning by learning to count"
    - "unsupervised learning of long term motion dynamics for videos"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"

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)