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: "beyond frontal faces improving person recognition using multiple cues"
  Abstract: We explore the task of recognizing peoples' identities in photo albums in an unconstrained setting. To facilitate this, we introduce the new People In Photo Albums (PIPA) dataset, consisting of over 60000 instances of 2000 individuals collected from public Flickr photo albums. With only about half of the person images containing a frontal face, the recognition task is very challenging due to the large variations in pose, clothing, camera viewpoint, image resolution and illumination. We propose the Pose Invariant PErson Recognition (PIPER) method, which accumulates the cues of poselet-level person recognizers trained by deep convolutional networks to discount for the pose variations, combined with a face recognizer and a global recognizer. Experiments on three different settings confirm that in our unconstrained setup PIPER significantly improves on the performance of DeepFace, which is one of the best face recognizers as measured on the LFW dataset.

Connected through hyperedge HE-1 (induced by source paper "person recognition in personal photo collections"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "pose aware person recognition"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "robust depth based person re identification"
    - "level playing field for million scale face recognition"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-2 (induced by source paper "triplet online instance matching loss for person re identification"):
  with members:
    - "multi pseudo regularized label for generated data in person re identification"
    - "query guided end to end person search"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "multi level factorisation net for person re identification"
    - "person search via a mask guided two stream cnn model"
    - "pyramidal person re identification via multi loss dynamic training"
    - "neural person search machines"
    - "pose normalized image generation for person re identification"

Connected through hyperedge HE-3 (induced by source paper "landmark detection in low resolution faces with semi supervised learning"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "look at boundary a boundary aware face alignment algorithm"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "spectral normalization for generative adversarial networks"
    - "low resolution face recognition"

Connected through hyperedge HE-4 (induced by source paper "overlearning reveals sensitive attributes"):
  with members:
    - "similarity of neural network representations revisited"
    - "privacy partitioning protecting user data during the deep learning inference phase"
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "exploiting unintended feature leakage in collaborative learning"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "isolating sources of disentanglement in variational autoencoders"
    - "privacy preserving neural representations of text"
    - "adversarial removal of demographic attributes from text data"

Connected through hyperedge HE-5 (induced by source paper "a graph based framework to bridge movies and synopses"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "person search in videos with one portrait through visual and temporal links"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "from trailers to storylines an efficient way to learn from movies"
    - "multilevel language and vision integration for text to clip retrieval"
    - "unifying identification and context learning for person recognition"
    - "holistic multi modal memory network for movie question answering"
    - "dual encoding for zero example video retrieval"

Connected through hyperedge HE-6 (induced by source paper "characteristic regularisation for super resolving face images"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "super identity convolutional neural network for face hallucination"
    - "low resolution face recognition"
    - "surveillance face recognition challenge"
    - "image to image translation for domain adaptation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "query guided end to end person search"):
  with members:
    - "person search via a mask guided two stream cnn model"
    - "focal loss for dense object detection"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "neural person search machines"
    - "squeeze and excitation networks"
    - "end to end comparative attention networks for person re identification"
    - "ian the individual aggregation network for person search"
    - "mask r cnn"

Connected through hyperedge HE-8 (induced by source paper "de identification without losing faces"):
  with members:
    - "natural and effective obfuscation by head inpainting"
    - "unsupervised image to image translation networks"
    - "face deidentification with generative deep neural networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "deep residual learning for image 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)