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: "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
  Abstract: We have recently witnessed tremendous success of Machine Learning (ML) in practical applications. Computer vision, speech recognition and language translation have all seen a near human level performance. We expect, in the near future, most business applications will have some form of ML. However, testing such applications is extremely challenging and would be very expensive if we follow today's methodologies. In this work, we present an articulation of the challenges in testing ML based applications. We then present our solution approach, based on the concept of Metamorphic Testing, which aims to identify implementation bugs in ML based image classifiers. We have developed metamorphic relations for an application based on Support Vector Machine and a Deep Learning based application. Empirical validation showed that our approach was able to catch 71% of the implementation bugs in the ML applications.

Connected through hyperedge HE-1 (induced by source paper "reliability validation of learning enabled vehicle tracking"):
  with members:
    - "test metrics for recurrent neural networks"
    - "reasoning about cognitive trust in stochastic multiagent systems"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "detecting and tracking small moving objects in wide area motion imagery wami using convolutional neural networks cnns"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "object based metamorphic testing through image structuring"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "adaptive metamorphic testing with contextual bandits"

Connected through hyperedge HE-3 (induced by source paper "metamorphic testing for object detection systems"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "yolact real time instance segmentation"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "deep learning for generic object detection a survey"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-4 (induced by source paper "software engineering meets deep learning a literature review"):
  with members:
    - "feature maps a comprehensible software representation for design pattern detection"
    - "on learning meaningful code changes via neural machine translation"
    - "on using machine learning to identify knowledge in api reference documentation"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "when deep learning met code search"
    - "a comprehensive study on deep learning bug characteristics"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "csindexbr exploring the brazilian scientific production in computer science"

Connected through hyperedge HE-5 (induced by source paper "testing deep learning models for image analysis using object relevant metamorphic relations"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "bag of freebies for training object detection neural networks"
    - "cornernet lite efficient keypoint based object detection"
    - "bag of tricks for image classification with convolutional neural networks"
    - "yolov3 an incremental improvement"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "simple baselines for human pose estimation and tracking"
    - "deepmutation mutation testing of deep learning systems"

Connected through hyperedge HE-6 (induced by source paper "metamorphic testing of a deep learning based forecaster"):
  with members:
    - "the elephant in the room"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "exploring the landscape of spatial robustness"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "developing bug free machine learning systems with formal mathematics"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-7 (induced by source paper "adaptive metamorphic testing with contextual bandits"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "metamorphic detection of adversarial examples in deep learning models with affine transformations"
    - "metamorphic testing for quality assurance of protein function prediction tools"
    - "fault detection effectiveness of metamorphic relations developed for testing supervised classifiers"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-8 (induced by source paper "2l 3w 2 level 3 way hardware software co verification for the mapping of deep learning architecture dla onto fpga boards"):
  with members:
    - "detecting sybil attacks using proofs of work and location in vanets"
    - "privacy preserving smart parking system using blockchain and private information retrieval"
    - "b ride ride sharing with privacy preservation trust and fair payment atop public blockchain"
    - "privacy preserving and collusion resistant charging coordination schemes for smart grid"
    - "on sharing models instead of data using mimic learning for smart health applications"
    - "mimic learning to generate a shareable network intrusion detection model"
    - "a stealthy hardware trojan exploiting the architectural vulnerability of deep learning architectures input interception attack iia"
    - "a scalable multilabel classification to deploy deep learning architectures for edge devices"

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.SE(Software Engineering)