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: "exploiting feature and class relationships in video categorization with regularized deep neural networks"
  Abstract: In this paper, we study the challenging problem of categorizing videos according to high-level semantics such as the existence of a particular human action or a complex event. Although extensive efforts have been devoted in recent years, most existing works combined multiple video features using simple fusion strategies and neglected the utilization of inter-class semantic relationships. This paper proposes a novel unified framework that jointly exploits the feature relationships and the class relationships for improved categorization performance. Specifically, these two types of relationships are estimated and utilized by imposing regularizations in the learning process of a deep neural network (DNN). Through arming the DNN with better capability of harnessing both the feature and the class relationships, the proposed regularized DNN (rDNN) is more suitable for modeling video semantics. We show that rDNN produces better performance over several state-of-the-art approaches. Competitive results are reported on the well-known Hollywood2 and Columbia Consumer Video benchmarks. In addition, to stimulate future research on large scale video categorization, we collect and release a new benchmark dataset, called FCVID, which contains 91,223 Internet videos and 239 manually annotated categories.

Connected through hyperedge HE-1 (induced by source paper "an end to end visual audio attention network for emotion recognition in user generated videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "affective computing for large scale heterogeneous multimedia data a survey"
    - "pdanet polarity consistent deep attention network for fine grained visual emotion regression"
    - "human centered emotion recognition in animated gifs"
    - "deep learning for sentiment analysis a survey"
    - "a multi task neural approach for emotion attribution classification and summarization"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-2 (induced by source paper "clean label backdoor attacks on video recognition models"):
  with members:
    - "skip connections matter on the transferability of adversarial examples generated with resnets"
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "black box adversarial attacks on video recognition models"
    - "spectral signatures in backdoor attacks"
    - "robust physical world attacks on deep learning models"
    - "certified defenses for data poisoning attacks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "targeted backdoor attacks on deep learning systems using data poisoning"

Connected through hyperedge HE-3 (induced by source paper "fastva deep learning video analytics through edge processing and npu in mobile"):
  with members:
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "cnndroid gpu accelerated execution of trained deep convolutional neural networks on android"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "liteeval a coarse to fine framework for resource efficient video recognition"):
  with members:
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "autofocus efficient multi scale inference"
    - "adaframe adaptive frame selection for fast video recognition"
    - "sniper efficient multi scale training"
    - "slowfast networks for video recognition"
    - "skipnet learning dynamic routing in convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "adapnet adaptability decomposing encoder decoder network for weakly supervised action recognition and localization"):
  with members:
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "modeling multimodal clues in a hybrid deep learning framework for video classification"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-6 (induced by source paper "black box adversarial attacks on video recognition models"):
  with members:
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "targeted nonlinear adversarial perturbations in images and videos"
    - "sparse adversarial perturbations for videos"
    - "black box adversarial attacks with limited queries and information"
    - "adversarial perturbations against real time video classification systems"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "modeling multimodal clues in a hybrid deep learning framework for video classification"

Connected through hyperedge HE-7 (induced by source paper "mfas multimodal fusion architecture search"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "centralnet a multilayer approach for multimodal fusion"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "efficient progressive neural architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"

Connected through hyperedge HE-8 (induced by source paper "learning to recommend with multiple cascading behaviors"):
  with members:
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "graph neural networks for social recommendation"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "adversarial personalized ranking for recommendation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "self attentive sequential recommendation"
    - "neural collaborative filtering"

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)