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: "continuous action recognition based on sequence alignment"
  Abstract: Continuous action recognition is more challenging than isolated recognition because classification and segmentation must be simultaneously carried out. We build on the well known dynamic time warping framework and devise a novel visual alignment technique, namely dynamic frame warping (DFW), which performs isolated recognition based on per-frame representation of videos, and on aligning a test sequence with a model sequence. Moreover, we propose two extensions which enable to perform recognition concomitant with segmentation, namely one-pass DFW and two-pass DFW. These two methods have their roots in the domain of continuous recognition of speech and, to the best of our knowledge, their extension to continuous visual action recognition has been overlooked. We test and illustrate the proposed techniques with a recently released dataset (RAVEL) and with two public-domain datasets widely used in action recognition (Hollywood-1 and Hollywood-2). We also compare the performances of the proposed isolated and continuous recognition algorithms with several recently published methods.

Connected through hyperedge HE-1 (induced by source paper "online real time multiple spatiotemporal action localisation and prediction"):
  with members:
    - "fast optical flow using dense inverse search"
    - "online action detection"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "deep learning for detecting multiple space time action tubes in videos"
    - "towards weakly supervised action localization"
    - "actionness estimation using hybrid fully convolutional networks"
    - "untrimmed video classification for activity detection submission to activitynet challenge"

Connected through hyperedge HE-2 (induced by source paper "shapedtw shape dynamic time warping"):
  with members:
    - "the great time series classification bake off an experimental evaluation of recently proposed algorithms extended version"
    - "an experimental evaluation of nearest neighbour time series classification"
    - "invariant time series factorization"
    - "metric learning for temporal sequence alignment"
    - "a time series forest for classification and feature extraction"
    - "sdtw computing dtw distances using locally relevant constraints based on salient feature alignments"
    - "faster retrieval with a two pass dynamic time warping lower bound"

Connected through hyperedge HE-3 (induced by source paper "metricdtw local distance metric learning in dynamic time warping"):
  with members:
    - "an experimental evaluation of nearest neighbour time series classification"
    - "metric learning for temporal sequence alignment"
    - "a survey on metric learning for feature vectors and structured data"
    - "unsupervised discovery of mid level discriminative patches"

Connected through hyperedge HE-4 (induced by source paper "online action recognition based on incremental learning of weighted covariance descriptors"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "hopc histogram of oriented principal components of 3d pointclouds for action recognition"
    - "spatio temporal covariance descriptors for action and gesture recognition"

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)