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: "recognizing plans by learning embeddings from observed action distributions"
  Abstract: Recent advances in visual activity recognition have raised the possibility of applications such as automated video surveillance. Effective approaches for such problems however require the ability to recognize the plans of the agents from video information. Although traditional plan recognition algorithms depend on access to sophisticated domain models, one recent promising direction involves learning shallow models directly from the observed activity sequences, and using them to recognize/predict plans. One limitation of such approaches is that they expect observed action sequences as training data. In many cases involving vision or sensing from raw data, there is considerably uncertainty about the specific action at any given time point. The most we can expect in such cases is probabilistic information about the action at that point. The training data will then be sequences of such observed action distributions. In this paper, we focus on doing effective plan recognition with such uncertain observations. Our contribution is a novel extension of word vector embedding techniques to directly handle such observation distributions as input. This involves computing embeddings by minimizing the distance between distributions (measured as KL-divergence). We will show that our approach has superior performance when the perception error rate (PER) is higher, and competitive performance when the PER is lower. We will also explore the possibility of using importance sampling techniques to handle observed action distributions with traditional word vector embeddings. We will show that although such approaches can give good recognition accuracy, they take significantly longer training time and their performance will degrade significantly at higher perception error rate.

Connected through hyperedge HE-1 (induced by source paper "challenges of human aware ai systems"):
  with members:
    - "planning with explanatory actions a joint approach to plan explicability and explanations in human aware planning"
    - "explicability legibility predictability transparency privacy security the emerging landscape of interpretable agent behavior"
    - "plan explanations as model reconciliation an empirical study"
    - "a unified framework for planning in adversarial and cooperative environments"
    - "ai challenges in human robot cognitive teaming"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "discovering underlying plans based on distributed representations of actions"
    - "plan explicability and predictability for robot task planning"

Connected through hyperedge HE-2 (induced by source paper "agent embeddings a latent representation for pole balancing networks"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "visual interpretability for deep learning a survey"
    - "programmatically interpretable reinforcement learning"
    - "policy certificates towards accountable reinforcement learning"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "dna gan learning disentangled representations from multi attribute images"
    - "neural networks designing neural networks multi objective hyper parameter optimization"

Connected through hyperedge HE-3 (induced by source paper "plan recognition driven attention modeling for visual recognition"):
  with members:
    - "hierarchical attentive recurrent tracking"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "attentional pooling for action recognition"
    - "spatial transformer networks"
    - "discovering underlying plans based on distributed representations of actions"
    - "learning deconvolution network for semantic segmentation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "feedforward semantic segmentation with zoom out features"

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.AI(Artificial Intelligence)