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: "trajectory aligned features for first person action recognition"
  Abstract: Egocentric videos are characterised by their ability to have the first person view. With the popularity of Google Glass and GoPro, use of egocentric videos is on the rise. Recognizing action of the wearer from egocentric videos is an important problem. Unstructured movement of the camera due to natural head motion of the wearer causes sharp changes in the visual field of the egocentric camera causing many standard third person action recognition techniques to perform poorly on such videos. Objects present in the scene and hand gestures of the wearer are the most important cues for first person action recognition but are difficult to segment and recognize in an egocentric video. We propose a novel representation of the first person actions derived from feature trajectories. The features are simple to compute using standard point tracking and does not assume segmentation of hand/objects or recognizing object or hand pose unlike in many previous approaches. We train a bag of words classifier with the proposed features and report a performance improvement of more than 11% on publicly available datasets. Although not designed for the particular case, we show that our technique can also recognize wearer's actions when hands or objects are not visible.

Connected through hyperedge HE-1 (induced by source paper "three stream fusion network for first person interaction recognition"):
  with members:
    - "robust and discriminative labeling for multi label active learning based on maximum correntropy criterion"
    - "spatiotemporal pyramid network for video action recognition"
    - "non linear temporal subspace representations for activity recognition"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "boosted multiple kernel learning for first person activity recognition"
    - "attentional pooling for action recognition"
    - "convolutional long short term memory networks for recognizing first person interactions"
    - "action recognition with coarse to fine deep feature integration and asynchronous fusion"

Connected through hyperedge HE-2 (induced by source paper "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"):
  with members:
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "unsupervised temporal feature aggregation for event detection in unstructured sports videos"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "smarttennistv automatic indexing of tennis videos"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "towards structured analysis of broadcast badminton videos"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "segmental spatiotemporal cnns for fine grained action segmentation"

Connected through hyperedge HE-4 (induced by source paper "making third person techniques recognize first person actions in egocentric videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "compact cnn for indexing egocentric videos"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "mo2cap2 real time mobile 3d motion capture with a cap mounted fisheye camera"):
  with members:
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "learning from synthetic humans"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "generating multiple diverse hypotheses for human 3d pose consistent with 2d joint detections"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "towards structured analysis of broadcast badminton videos"):
  with members:
    - "am i a baller basketball performance assessment from first person videos"
    - "segmental spatiotemporal cnns for fine grained action segmentation"
    - "wavenet a generative model for raw audio"
    - "temporal convolutional networks a unified approach to action segmentation"
    - "learning to track at 100 fps with deep regression networks"
    - "deep residual learning for image recognition"
    - "a hierarchical deep temporal model for group activity recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

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)