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: "deep feature based face detection on mobile devices"
  Abstract: We propose a deep feature-based face detector for mobile devices to detect user's face acquired by the front facing camera. The proposed method is able to detect faces in images containing extreme pose and illumination variations as well as partial faces. The main challenge in developing deep feature-based algorithms for mobile devices is the constrained nature of the mobile platform and the non-availability of CUDA enabled GPUs on such devices. Our implementation takes into account the special nature of the images captured by the front-facing camera of mobile devices and exploits the GPUs present in mobile devices without CUDA-based frameorks, to meet these challenges.

Connected through hyperedge HE-1 (induced by source paper "is your smartband smart enough to know who you are towards continuous physiological authentication in the wild"):
  with members:
    - "waca wearable assisted continuous authentication"

Connected through hyperedge HE-2 (induced by source paper "partial face detection in the mobile domain"):
  with members:
    - "pooling facial segments to face the shallow and deep ends"
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "face detection with end to end integration of a convnet and a 3d model"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "ssd single shot multibox detector"
    - "a fast and accurate unconstrained face detector"
    - "partial face detection for continuous authentication"
    - "convolutional neural networks for attribute based active authentication on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "pooling facial segments to face the shallow and deep ends"):
  with members:
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "a fast and accurate unconstrained face detector"
    - "partial face detection for continuous authentication"
    - "convolutional neural networks for attribute based active authentication on mobile devices"
    - "an all in one convolutional neural network for face analysis"
    - "multi view face detection using deep convolutional neural networks"
    - "a deep pyramid deformable part model for face detection"

Connected through hyperedge HE-4 (induced by source paper "active user authentication for smartphones a challenge data set and benchmark results"):
  with members:
    - "active authentication on mobile devices via stylometry application usage web browsing and gps location"
    - "face alignment by local deep descriptor regression"
    - "partial face detection for continuous authentication"
    - "unconstrained face verification using deep cnn features"
    - "a deep pyramid deformable part model for face detection"
    - "learning human identity from motion patterns"
    - "learning face representation from scratch"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"

Connected through hyperedge HE-5 (induced by source paper "convolutional neural networks for attribute based active authentication on mobile devices"):
  with members:
    - "partial face detection for continuous authentication"
    - "facenet a unified embedding for face recognition and clustering"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"
    - "panda pose aligned networks for deep attribute modeling"

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)