human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "discovering underlying plans based on distributed representations of actions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep graph representation learning")
  Member "word2vec parameter learning explained":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "roulette wheel selection via stochastic acceptance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "go explore a new approach for hard exploration problems")

Question: Please tell me which class the source paper 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)