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: "adding attentiveness to the neurons in recurrent neural networks"
  Abstract: Recurrent neural networks (RNNs) are capable of modeling the temporal dynamics of complex sequential information. However, the structures of existing RNN neurons mainly focus on controlling the contributions of current and historical information but do not explore the different importance levels of different elements in an input vector of a time slot. We propose adding a simple yet effective Element-wiseAttention Gate (EleAttG) to an RNN block (e.g., all RNN neurons in a network layer) that empowers the RNN neurons to have the attentiveness capability. For an RNN block, an EleAttG is added to adaptively modulate the input by assigning different levels of importance, i.e., attention, to each element/dimension of the input. We refer to an RNN block equipped with an EleAttG as an EleAtt-RNN block. Specifically, the modulation of the input is content adaptive and is performed at fine granularity, being element-wise rather than input-wise. The proposed EleAttG, as an additional fundamental unit, is general and can be applied to any RNN structures, e.g., standard RNN, Long Short-Term Memory (LSTM), or Gated Recurrent Unit (GRU). We demonstrate the effectiveness of the proposed EleAtt-RNN by applying it to the action recognition tasks on both 3D human skeleton data and RGB videos. Experiments show that adding attentiveness through EleAttGs to RNN blocks significantly boosts the power of RNNs.

Connected through hyperedge HE-1 (induced by source paper "a survey on 3d skeleton based action recognition using learning method"):
  with members:
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "richlt activated graph convolutional network for action recognition with incomplete skeletons"
    - "make skeleton based action recognition model smaller faster and better"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "skelemotion a new representation of skeleton joint sequences based on motion information for 3d action recognition"

Connected through hyperedge HE-2 (induced by source paper "chirality nets for human pose regression"):
  with members:
    - "pic permutation invariant critic for multi agent deep reinforcement learning"
    - "unsupervised learning of object structure and dynamics from videos"
    - "on the convergence of adam and beyond"
    - "ordinal depth supervision for 3d human pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "action agnostic human pose forecasting"

Connected through hyperedge HE-3 (induced by source paper "semantics guided neural networks for efficient skeleton based human action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "eleatt rnn adding attentiveness to neurons in recurrent neural networks"
    - "deep high resolution representation learning for human pose estimation"
    - "learning deep bilinear transformation for fine grained image representation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-4 (induced by source paper "eleatt rnn adding attentiveness to neurons in recurrent neural networks"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "cbam convolutional block attention module"
    - "attention is all you need"
    - "squeeze and excitation networks"

Connected through hyperedge HE-5 (induced by source paper "human vs machine attention in neural networks a comparative study"):
  with members:
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "reciprocal attention fusion for visual question answering"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "fine grained video categorization with redundancy reduction attention"
    - "reverse attention for salient object detection"
    - "learn to pay attention"
    - "cbam convolutional block attention module"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "non local recurrent neural memory for supervised sequence modeling"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "relational recurrent neural networks"
    - "high order recurrent neural networks for acoustic modelling"
    - "glomo unsupervisedly learned relational graphs as transferable representations"
    - "learning to generate reviews and discovering sentiment"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "non local neural networks"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"):
  with members:
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "when will you do what anticipating temporal occurrences of activities"
    - "lsta long short term attention for egocentric action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "forecasting hand and object locations in future frames"
    - "am i done predicting action progress in videos"
    - "encouraging lstms to anticipate actions very early"
    - "visual forecasting by imitating dynamics in natural sequences"

Connected through hyperedge HE-8 (induced by source paper "multi grained spatio temporal modeling for lip reading"):
  with members:
    - "end to end audiovisual speech recognition"
    - "lrw 1000 a naturally distributed large scale benchmark for lip reading in the wild"
    - "lipnet end to end sentence level lipreading"
    - "combining residual networks with lstms for lipreading"
    - "convolutional long short term memory networks for recognizing first person interactions"
    - "lip reading sentences in the wild"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "two stream convolutional networks for action recognition in videos"

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)