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 "a transfer learning method for goal recognition exploiting cross domain spatial features"):
  Abstract: The ability to infer the intentions of others, predict their goals, and deduce their plans are critical features for intelligent agents. For a long time, several approaches investigated the use of symbolic representations and inferences with limited success, principally because it is difficult to capture the cognitive knowledge behind human decisions explicitly. The trend, nowadays, is increasingly focusing on learning to infer intentions directly from data, using deep learning in particular. We are now observing interesting applications of intent classification in natural language processing, visual activity recognition, and emerging approaches in other domains. This paper discusses a novel approach combining few-shot and transfer learning with cross-domain features, to learn to infer the intent of an agent navigating in physical environments, executing arbitrary long sequences of actions to achieve their goals. Experiments in synthetic environments demonstrate improved performance in terms of learning from few samples and generalizing to unseen configurations, compared to a deep-learning baseline approach.

Includes members:
  Member "convolutional neural networks with layer reuse":
    also connected through other hyperedges:
      (none)
  Member "deep learning for sensor based activity recognition a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "uncertainty quantification for deep context aware mobile activity recognition and unknown context discovery")
  Member "human action recognition and prediction a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation")
  Member "transfer learning from speaker verification to multispeaker text to speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parallel neural text to speech")
  Member "a survey on deep transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "lstm based goal recognition in latent space":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural symbolic descriptive action model from images the search for strips")
  Member "heuristic online goal recognition in continuous domains":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "landmark based approaches for goal recognition as planning")
  Member "latent intention dialogue models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue")

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)