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 "animalweb a large scale hierarchical dataset of annotated animal faces"):
  Abstract: Being heavily reliant on animals, it is our ethical obligation to improve their well-being by understanding their needs. Several studies show that animal needs are often expressed through their faces. Though remarkable progress has been made towards the automatic understanding of human faces, this has regrettably not been the case with animal faces. There exists significant room and appropriate need to develop automatic systems capable of interpreting animal faces. Among many transformative impacts, such a technology will foster better and cheaper animal healthcare, and further advance animal psychology understanding. #R##N#We believe the underlying research progress is mainly obstructed by the lack of an adequately annotated dataset of animal faces, covering a wide spectrum of animal species. To this end, we introduce a large-scale, hierarchical annotated dataset of animal faces, featuring 21.9K faces from 334 diverse species and 21 animal orders across biological taxonomy. These faces are captured `in-the-wild' conditions and are consistently annotated with 9 landmarks on key facial features. The proposed dataset is structured and scalable by design; its development underwent four systematic stages involving rigorous, manual annotation effort of over 6K man-hours. We benchmark it for face alignment using the existing art under novel problem settings. Results showcase its challenging nature, unique attributes and present definite prospects for novel, adaptive, and generalized face-oriented CV algorithms. We further benchmark the dataset for face detection and fine-grained recognition tasks, to demonstrate multi-task applications and room for improvement. Experiments indicate that this dataset will push the algorithmic advancements across many related CV tasks and encourage the development of novel systems for animal facial behaviour monitoring. We will make the dataset publicly available.

Includes members:
  Member "quantized densely connected u nets for efficient landmark localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  Member "multi attention multi class constraint for fine grained image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "first order motion model for image animation")
  Member "interspecies knowledge transfer for facial keypoint detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transferring dense pose to proximal animal classes")
  Member "the inaturalist species classification and detection dataset":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "human and sheep facial landmarks localisation by triplet interpolated features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transferring dense pose to proximal animal classes")
  Member "occlusion coherence detecting and localizing occluded faces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust facial landmark detection via aggregation on geometrically manipulated faces")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)