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: "anticipation and next action forecasting in video an end to end model with memory"
  Abstract: Action anticipation and forecasting in videos do not require a hat-trick, as far as there are signs in the context to foresee how actions are going to be deployed. Capturing these signs is hard because the context includes the past. We propose an end-to-end network for action anticipation and forecasting with memory, to both anticipate the current action and foresee the next one. Experiments on action sequence datasets show excellent results indicating that training on histories with a dynamic memory can significantly improve forecasting performance.

Connected through hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "videograph recognizing minutes long human activities in videos"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "binge watching scaling affordance learning from sitcoms"
    - "charades ego a large scale dataset of paired third and first person videos"

Connected through hyperedge HE-2 (induced by source paper "action anticipation for collaborative environments the impact of contextual information and uncertainty based prediction"):
  with members:
    - "delving into 3d action anticipation from streaming videos"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "early action prediction with generative adversarial networks"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "human action recognition and prediction a survey"
    - "action anticipation by predicting future dynamic images"
    - "anticipation in human robot cooperation a recurrent neural network approach for multiple action sequences prediction"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "forecasting future action sequences with neural memory networks"):
  with members:
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "when will you do what anticipating temporal occurrences of activities"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "tree memory networks for modelling long term temporal dependencies"
    - "encouraging lstms to anticipate actions very early"
    - "red reinforced encoder decoder networks for action anticipation"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "segmental spatiotemporal cnns for fine grained action segmentation"

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)