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: "learning long term dependencies for action recognition with a biologically inspired deep network"
  Abstract: Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task. As one of the key models for sequence learning, recurrent neural network (RNN) and its variants such as long short term memory (LSTM) and gated recurrent unit (GRU) are still not powerful enough in practice. One possible reason is that they have only feedforward connections, which is different from the biological neural system that is typically composed of both feedforward and feedback connections. To address this problem, this paper proposes a biologically-inspired deep network, called shuttleNet\footnote{Our code is available at \url{this https URL}}. Technologically, the shuttleNet consists of several processors, each of which is a GRU while associated with multiple groups of cells and states. Unlike traditional RNNs, all processors inside shuttleNet are loop connected to mimic the brain's feedforward and feedback connections, in which they are shared across multiple pathways in the loop connection. Attention mechanism is then employed to select the best information flow pathway. Extensive experiments conducted on two benchmark datasets (i.e UCF101 and HMDB51) show that we can beat state-of-the-art methods by simply embedding shuttleNet into a CNN-RNN framework.

Connected through hyperedge HE-1 (induced by source paper "deep concept wise temporal convolutional networks for action localization"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "activitynet challenge 2017 summary"
    - "single shot temporal action detection"

Connected through hyperedge HE-2 (induced by source paper "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"):
  with members:
    - "read watch and move reinforcement learning for temporally grounding natural language descriptions in videos"
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "fine grained video categorization with redundancy reduction attention"
    - "motion feature network fixed motion filter for action recognition"
    - "compact generalized non local network"
    - "adaframe adaptive frame selection for fast video recognition"

Connected through hyperedge HE-3 (induced by source paper "p odn prototype based open deep network for open set recognition"):
  with members:
    - "robust classification with convolutional prototype learning"
    - "generative openmax for multi class open set classification"
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "temporal segment networks towards good practices for deep action recognition"
    - "tensorflow a system for large scale machine learning"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "towards open set deep networks"
    - "towards open world recognition"

Connected through hyperedge HE-4 (induced by source paper "semantic adversarial network with multi scale pyramid attention for video classification"):
  with members:
    - "shared predictive cross modal deep quantization"
    - "triplet based deep hashing network for cross modal retrieval"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "compressed video action recognition"
    - "temporal convolutional networks for action segmentation and detection"

Connected through hyperedge HE-5 (induced by source paper "learning discriminative video representations using adversarial perturbations"):
  with members:
    - "non linear temporal subspace representations for activity recognition"
    - "video representation learning using discriminative pooling"
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "lattice long short term memory for human action recognition"

Connected through hyperedge HE-6 (induced by source paper "stnet local and global spatial temporal modeling for action recognition"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "a short note about kinetics 600"
    - "yh technologies at activitynet challenge 2018"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "appearance and relation networks for video classification"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised action localization by sparse temporal pooling network"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "temporal action localization by structured maximal sums"

Connected through hyperedge HE-8 (induced by source paper "compressed video action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "lattice long short term memory for human action recognition"
    - "attentional pooling for action recognition"
    - "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"
    - "convnet architecture search for spatiotemporal feature learning"

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)