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: "low resolution face recognition in the wild via selective knowledge distillation"
  Abstract: Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation. In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the fine-tuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources: approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification. Experimental results show that the student stream performs impressively in recognizing low-resolution faces and costs only 0.15-MB memory and runs at 418 faces per second on CPU and 9433 faces per second on GPU.

Connected through hyperedge HE-1 (induced by source paper "attribute guided coupled gan for cross resolution face recognition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "learning to authenticate with deep multibiometric hashing and neural network decoding"
    - "adversarial examples to fool iris recognition systems"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "style and content disentanglement in generative adversarial networks"
    - "synthesis based robust low resolution face recognition"

Connected through hyperedge HE-2 (induced by source paper "graph based knowledge distillation by multi head self attention network"):
  with members:
    - "self supervised knowledge distillation using singular value decomposition"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "knowledge transfer via distillation of activation boundaries formed by hidden neurons"
    - "linguistically informed self attention for semantic role labeling"
    - "non local neural networks"
    - "attention is all you need"
    - "long short term memory networks for machine reading"

Connected through hyperedge HE-3 (induced by source paper "extreme low resolution activity recognition with spatial temporal attention transfer"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "efficient video classification using fewer frames"
    - "fully coupled two stream spatiotemporal networks for extremely low resolution action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-4 (induced by source paper "cross resolution face recognition via prior aided face hallucination and residual knowledge distillation"):
  with members:
    - "esrgan enhanced super resolution generative adversarial networks"
    - "look across elapse disentangled representation learning and photorealistic cross age face synthesis for age invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "super identity convolutional neural network for face hallucination"
    - "adversarial network compression"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "adversarial discriminative heterogeneous face recognition"
    - "cross age lfw a database for studying cross age face recognition in unconstrained environments"

Connected through hyperedge HE-5 (induced by source paper "dual directed capsule network for very low resolution image recognition"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "brain tumor type classification via capsule networks"
    - "recognizing disguised faces in the wild"
    - "dynamic routing between capsules"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "studying very low resolution recognition using deep networks"
    - "enhancenet single image super resolution through automated texture synthesis"

Connected through hyperedge HE-6 (induced by source paper "improving route choice models by incorporating contextual factors via knowledge distillation"):
  with members:
    - "learning efficient detector with semi supervised adaptive distillation"
    - "pixel level reconstruction and classification for noisy handwritten bangla characters"
    - "unsupervised learning using pretrained cnn and associative memory bank"
    - "deep mutual learning"
    - "safernet safe transportation routing in the era of internet of vehicles and mobile crowd sensing"
    - "deepsat a learning framework for satellite imagery"

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)