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: "mdssd multi scale deconvolutional single shot detector for small objects"
  Abstract: In order to improve the detection accuracy for objects at different scales, most of the recent works utilize the pyramidal feature hierarchy of the ConvNets from bottom to top. Nevertheless, the weak semantic information makes the bottom layers poor in detection, especially for small objects. Furthermore, most of the fine details are lost on the top layers. In this paper, we design a Multi-scale Deconvolutional Single Shot Detector for small objects (MDSSD for short). To obtain the feature maps with enriched representation power, we add the high-level layers with semantic information to the low-level layers via deconvolution Fusion Block. It is noteworthy that multiple high-level layers with different scales are upsampled simultaneously in our framework. We implement the skip connections to form more descriptive feature maps and predictions are made on these new fusion layers. Our proposed framework achieves 78.6% mAP on PASCAL VOC2007 test and 26.8% mAP on MS COCO test-dev2015 at 38.5 FPS with only 300*300 input.

Connected through hyperedge HE-1 (induced by source paper "towards adversarially robust object detection"):
  with members:
    - "rethinking imagenet pre training"
    - "robust adversarial perturbation on deep proposal based models"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "feature denoising for improving adversarial robustness"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "yolov3 an incremental improvement"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

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)