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: "knowledge based recurrent attentive neural network for traffic sign detection"
  Abstract: Accurate Traffic Sign Detection (TSD) can help drivers make better decision according to the traffic regulations. TSD, regarded as a typical small object detection problem in some way, is fundamental in the field of self-driving and advanced driver assistance systems. However, small object detection is still an open question. In this paper, we proposed a human brain inspired network to handle this problem. Attention mechanism is an essential function of our brain, we used a novel recurrent attentive neural network to improve the detection accuracy in a fine-grained manner. Further, as we human can combine domain specific knowledge and intuitive knowledge to solve tricky tasks, we proposed an assumption that the location of the traffic signs obeys the reverse gaussian distribution, which means the location is around the central bias of every picture. Experimental result shows that our methods achieved better performance than several popular methods used in object detection.

Connected through hyperedge HE-1 (induced by source paper "knowledge infused learning k il towards deep incorporation of knowledge in deep learning"):
  with members:
    - "infusing domain knowledge in ai based black box models for better explainability with application in bankruptcy prediction"
    - "ernie enhanced representation through knowledge integration"
    - "predictive analysis on twitter techniques and applications"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "modeling islamist extremist communications on social media using contextual dimensions religion ideology and hate"
    - "k bert enabling language representation with knowledge graph"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "ai reasoning systems pac and applied methods"

Connected through hyperedge HE-2 (induced by source paper "traffic sign detection under challenging conditions a deeper look into performance variations and spectral characteristics"):
  with members:
    - "distorted representation space characterization through backpropagated gradients"
    - "challenging environments for traffic sign detection reliability assessment under inclement conditions"
    - "object recognition under multifarious conditions a reliability analysis and a feature similarity based performance estimation"
    - "traffic signs in the wild highlights from the ieee video and image processing cup 2017 student competition sp competitions"
    - "cure or challenging unreal and real environments for object recognition"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "cure tsr challenging unreal and real environments for traffic sign recognition"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"

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)