human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "forecasting future action sequences with neural memory networks"):
  Abstract: We propose a novel neural memory network based framework for future action sequence forecasting. This is a challenging task where we have to consider short-term, within sequence relationships as well as relationships in between sequences, to understand how sequences of actions evolve over time. To capture these relationships effectively, we introduce neural memory networks to our modelling scheme. We show the significance of using two input streams, the observed frames and the corresponding action labels, which provide different information cues for our prediction task. Furthermore, through the proposed method we effectively map the long-term relationships among individual input sequences through separate memory modules, which enables better fusion of the salient features. Our method outperforms the state-of-the-art approaches by a large margin on two publicly available datasets: Breakfast and 50 Salads.

Includes members:
  Member "coupled generative adversarial network for continuous fine grained action segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "predicting the future a jointly learnt model for action anticipation")
  Member "anticipation and next action forecasting in video an end to end model with memory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "when will you do what anticipating temporal occurrences of activities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "weakly supervised action learning with rnn based fine to coarse modeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "tree memory networks for modelling long term temporal dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploiting human social cognition for the detection of fake and fraudulent faces via memory networks")
  Member "encouraging lstms to anticipate actions very early":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ttpp temporal transformer with progressive prediction for efficient action anticipation")
  Member "red reinforced encoder decoder networks for action anticipation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ttpp temporal transformer with progressive prediction for efficient action anticipation")
  Member "two stream lstm a deep fusion framework for human action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "automatic video object segmentation via motion appearance stream fusion and instance aware segmentation")

Question: Please tell me which class the source paper 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)