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: "analyzing human human interactions a survey"
  Abstract: Many videos depict people, and it is their interactions that inform us of their activities, relation to one another and the cultural and social setting. With advances in human action recognition, researchers have begun to address the automated recognition of these human-human interactions from video. The main challenges stem from dealing with the considerable variation in recording setting, the appearance of the people depicted and the coordinated performance of their interaction. This survey provides a summary of these challenges and datasets to address these, followed by an in-depth discussion of relevant vision-based recognition and detection methods. We focus on recent, promising work based on deep learning and convolutional neural networks (CNNs). Finally, we outline directions to overcome the limitations of the current state-of-the-art to analyze and, eventually, understand social human actions.

Connected through hyperedge HE-1 (induced by source paper "learning class regularized features for action recognition"):
  with members:
    - "class feature pyramids for video explanation"
    - "visualizing and understanding generative adversarial networks extended abstract"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "large scale weakly supervised pre training for video action recognition"
    - "multi fiber networks for video recognition"
    - "explaining explanations an overview of interpretability of machine learning"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "saliency tubes visual explanations for spatio temporal convolutions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "methods for interpreting and understanding deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "excitation backprop for rnns"
    - "interpreting cnn knowledge via an explanatory graph"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal fast 3d convolutions for human action recognition"):
  with members:
    - "multi fiber networks for video recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "learning transferable architectures for scalable image recognition"
    - "squeeze and excitation networks"
    - "the kinetics human action video dataset"
    - "depthwise separable convolutions for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "you2me inferring body pose in egocentric video via first and second person interactions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "simple baselines for human pose estimation and tracking"
    - "detecting and recognizing human object interactions"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "learning feature pyramids for human pose estimation"
    - "detect and track efficient pose estimation in videos"
    - "posetrack joint multi person pose estimation and tracking"

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)