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: "lstm based goal recognition in latent space"
  Abstract: Approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms capable of recognizing goals. However, to recognize goals in raw data, recent approaches require either human engineered domain knowledge, or samples of behavior that account for almost all actions being observed to infer possible goals. This is clearly too strong a requirement for real-world applications of goal recognition, and we develop an approach that leverages advances in recurrent neural networks to perform goal recognition as a classification task, using encoded plan traces for training. We empirically evaluate our approach against the state-of-the-art in goal recognition with image-based domains, and discuss under which conditions our approach is superior to previous ones.

Connected through hyperedge HE-1 (induced by source paper "neural symbolic descriptive action model from images the search for strips"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning action models from disordered and noisy plan traces"
    - "towards stable symbol grounding with zero suppressed state autoencoder"
    - "learning strips action models with classical planning"
    - "learning plannable representations with causal infogan"
    - "learning finite state representations of recurrent policy networks"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "dvae discrete variational autoencoders with overlapping transformations"

Connected through hyperedge HE-2 (induced by source paper "a transfer learning method for goal recognition exploiting cross domain spatial features"):
  with members:
    - "convolutional neural networks with layer reuse"
    - "deep learning for sensor based activity recognition a survey"
    - "human action recognition and prediction a survey"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "a survey on deep transfer learning"
    - "heuristic online goal recognition in continuous domains"
    - "latent intention dialogue models"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"

Connected through hyperedge HE-3 (induced by source paper "cost based goal recognition meets deep learning"):
  with members:
    - "heuristic online goal recognition in continuous domains"
    - "visual interaction networks"
    - "latent intention dialogue models"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "value iteration networks"
    - "adam a method for stochastic optimization"
    - "two stream convolutional networks for action recognition in videos"
    - "policy recognition in the abstract hidden markov model"

Connected through hyperedge HE-4 (induced by source paper "towards stable symbol grounding with zero suppressed state autoencoder"):
  with members:
    - "learning plannable representations with causal infogan"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "a survey of model compression and acceleration for deep neural networks"
    - "learning strips operators from noisy and incomplete observations"
    - "the ff planning system fast plan generation through heuristic search"
    - "the symbol grounding problem"

Connected through hyperedge HE-5 (induced by source paper "photo realistic blocksworld dataset"):
  with members:
    - "learning plannable representations with causal infogan"
    - "learning interpretable spatial operations in a rich 3d blocks world"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "the arcade learning environment an evaluation platform for general agents"

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)