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: "unconstrained face detection and open set face recognition challenge"
  Abstract: Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveillance cameras include more diverse occlusions, poses, weather conditions and image blur. Although face verification or closed-set face identification have surpassed human capabilities on some datasets, open-set identification is much more complex as it needs to reject both unknown identities and false accepts from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates. By contrast, open-set face recognition is currently weak and requires much more attention.

Connected through hyperedge HE-1 (induced by source paper "pixel in pixel net towards efficient facial landmark detection in the wild"):
  with members:
    - "how much position information do convolutional neural networks encode"
    - "semantic alignment finding semantically consistent ground truth for facial landmark detection"
    - "teacher supervises students how to learn from partially labeled images for facial landmark detection"
    - "aggregation via separation boosting facial landmark detector with semi supervised style translation"
    - "high resolution representations for labeling pixels and regions"
    - "look at boundary a boundary aware face alignment algorithm"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-2 (induced by source paper "low resolution face recognition in the wild via selective knowledge distillation"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "low resolution face recognition in the wild"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "synthesis based robust low resolution face recognition"
    - "data distillation towards omni supervised learning"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "sequence level knowledge distillation"

Connected through hyperedge HE-3 (induced by source paper "cross resolution learning for face recognition"):
  with members:
    - "super identity convolutional neural network for face hallucination"
    - "deep face recognition a survey"
    - "low resolution face recognition"
    - "additive margin softmax for face verification"
    - "surveillance face recognition challenge"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "synthesis based robust low resolution face recognition"
    - "low resolution face recognition using a two branch deep convolutional neural network architecture"

Connected through hyperedge HE-4 (induced by source paper "face detection on surveillance images"):
  with members:
    - "a fast and accurate unconstrained face detector"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "finding tiny faces"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "you only look once unified real time object detection"
    - "wider face a face detection benchmark"
    - "aggregate channel features for multi view face detection"

Connected through hyperedge HE-5 (induced by source paper "pushing the limits of unconstrained face detection a challenge dataset and baseline results"):
  with members:
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "face detection through scale friendly deep convolutional networks"
    - "ssh single stage headless face detector"
    - "image de raining using a conditional generative adversarial network"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "face detection with the faster r cnn"

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)