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: "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.

Connected through hyperedge HE-1 (induced by source paper "human action sequence classification"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "streamlined dense video captioning"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "weakly supervised gaussian networks for action detection"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "timeception for complex action recognition"
    - "temporal gaussian mixture layer for videos"

Connected through hyperedge HE-2 (induced by source paper "forecasting future sequence of actions to complete an activity"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "action anticipation by predicting future dynamic images"
    - "when will you do what anticipating temporal occurrences of activities"
    - "agent centric risk assessment accident anticipation and risky region localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "predicting human activities using stochastic grammar"
    - "encouraging lstms to anticipate actions very early"
    - "hollywood in homes crowdsourcing data collection for activity understanding"

Connected through hyperedge HE-3 (induced by source paper "human motion anticipation with symbolic label"):
  with members:
    - "learning trajectory dependencies for human motion prediction"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "modeling human motion with quaternion based neural networks"
    - "a neural temporal model for human motion prediction"
    - "quaternet a quaternion based recurrent model for human motion"
    - "convolutional sequence to sequence model for human dynamics"
    - "when will you do what anticipating temporal occurrences of activities"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence 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)