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: "prediction of manipulation actions"
  Abstract: Looking at a person's hands one often can tell what the person is going to do next, how his/her hands are moving and where they will be, because an actor's intentions shape his/her movement kinematics during action execution. Similarly, active systems with real-time constraints must not simply rely on passive video-segment classification, but they have to continuously update their estimates and predict future actions. In this paper, we study the prediction of dexterous actions. We recorded from subjects performing different manipulation actions on the same object, such as "squeezing", "flipping", "washing", "wiping" and "scratching" with a sponge. In psychophysical experiments, we evaluated human observers' skills in predicting actions from video sequences of different length, depicting the hand movement in the preparation and execution of actions before and after contact with the object. We then developed a recurrent neural network based method for action prediction using as input patches around the hand. We also used the same formalism to predict the forces on the finger tips using for training synchronized video and force data streams. Evaluations on two new datasets showed that our system closely matches human performance in the recognition task, and demonstrate the ability of our algorithm to predict what and how a dexterous action is performed.

Connected through hyperedge HE-1 (induced by source paper "a survey of knowledge representation in service robotics"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "the limits and potentials of deep learning for robotics"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "blitznet a real time deep network for scene understanding"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "affordancenet an end to end deep learning approach for object affordance detection"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of action classes with continuous temporal embedding"):
  with members:
    - "unsupervised learning and segmentation of complex activities from video"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "spatio temporal channel correlation networks for action classification"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "unsupervised video understanding by reconciliation of posture similarities"

Connected through hyperedge HE-3 (induced by source paper "sequential image based attention network for inferring force estimation without haptic sensor"):
  with members:
    - "cbam convolutional block attention module"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "admittance control parameter adaptation for physical human robot interaction"
    - "residual attention network for image classification"

Connected through hyperedge HE-4 (induced by source paper "long activity video understanding using functional object oriented network"):
  with members:
    - "functional object oriented network for manipulation learning"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "learning from multiple sources for video summarisation"
    - "an end to end generative framework for video segmentation and recognition"
    - "p cnn pose based cnn features for action recognition"
    - "structural rnn deep learning on spatio temporal graphs"
    - "tagbook a semantic video representation without supervision for event detection"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "am i done predicting action progress in videos"):
  with members:
    - "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations"
    - "action completion a temporal model for moment detection"
    - "guess where actor supervision for spatiotemporal action localization"
    - "temporal recurrent networks for online action detection"
    - "what actions are needed for understanding human actions in videos"
    - "temporal action detection with structured segment networks"
    - "action tubelet detector for spatio temporal action localization"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-6 (induced by source paper "first person hand action benchmark with rgb d videos and 3d hand pose annotations"):
  with members:
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "the 2017 hands in the million challenge on 3d hand pose estimation"
    - "real time hand tracking under occlusion from an egocentric rgb d sensor"
    - "depth based 3d hand pose estimation from current achievements to future goals"
    - "sembed semantic embedding of egocentric action videos"
    - "convolutional pose machines"

Connected through hyperedge HE-7 (induced by source paper "predicting human intentions from motion only a 2d 3d fusion approach"):
  with members:
    - "kernelized covariance for action recognition"
    - "revisiting human action recognition personalization vs generalization"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "p cnn pose based cnn features for action recognition"
    - "anticipating the future by watching unlabeled video"
    - "how transferable are features in deep neural networks"
    - "a discriminative cnn video representation for event detection"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

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)