human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "vision based body gesture meta features for affective computing"):
  Abstract: Early detection of psychological distress is key to effective treatment. Automatic detection of distress, such as depression, is an active area of research. Current approaches utilise vocal, facial, and bodily modalities. Of these, the bodily modality is the least investigated, partially due to the difficulty in extracting bodily representations from videos, and partially due to the lack of viable datasets. Existing body modality approaches use automatic categorization of expressions to represent body language as a series of specific expressions, much like words within natural language. In this dissertation I present a new type of feature, within the body modality, that represents meta information of gestures, such as speed, and use it to predict a non-clinical depression label. This differs to existing work by representing overall behaviour as a small set of aggregated meta features derived from a person's movement. In my method I extract pose estimation from videos, detect gestures within body parts, extract meta information from individual gestures, and finally aggregate these features to generate a small feature vector for use in prediction tasks. I introduce a new dataset of 65 video recordings of interviews with self-evaluated distress, personality, and demographic labels. This dataset enables the development of features utilising the whole body in distress detection tasks. I evaluate my newly introduced meta-features for predicting depression, anxiety, perceived stress, somatic stress, five standard personality measures, and gender. A linear regression based classifier using these features achieves a 82.70% F1 score for predicting depression within my novel dataset.

Includes members:
  Member "openpose realtime multi person 2d pose estimation using part affinity fields":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pose guided structured region ensemble network for cascaded hand pose estimation")
  Member "topic modeling based multi modal depression detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "avec 2019 workshop and challenge state of mind detecting depression with ai and cross cultural affect recognition")
  Member "hand keypoint detection in single images using multiview bootstrapping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency")
  Member "convolutional pose machines":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "realtime multi person 2d pose estimation using part affinity fields":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "cnn features off the shelf an astounding baseline for recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

Question: Please tell me which class the source paper 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)