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: "towards learning to perceive and reason about liquids"
  Abstract: Recent advances in AI and robotics have claimed many incredible results with deep learning, yet no work to date has applied deep learning to the problem of liquid perception and reasoning. In this paper, we apply fully-convolutional deep neural networks to the tasks of detecting and tracking liquids. We evaluate three models: a single-frame network, multi-frame network, and a LSTM recurrent network. Our results show that the best liquid detection results are achieved when aggregating data over multiple frames and that the LSTM network outperforms the other two in both tasks. This suggests that LSTM-based neural networks have the potential to be a key component for enabling robots to handle liquids using robust, closed-loop controllers.

Connected through hyperedge HE-1 (induced by source paper "intelligent autonomous things on the battlefield"):
  with members:
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "robust adversarial reinforcement learning"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the internet of battle things"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "how do you command an army of intelligent things"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-2 (induced by source paper "learning robotic manipulation of granular media"):
  with members:
    - "deep visual foresight for planning robot motion"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "cad2rl real single image flight without a single real image"
    - "visual closed loop control for pouring liquids"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "learning physical intuition of block towers by example"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-3 (induced by source paper "perceiving and reasoning about liquids using fully convolutional networks"):
  with members:
    - "lstm a search space odyssey"
    - "brain tumor segmentation with deep neural networks"
    - "reasoning about liquids via closed loop simulation"
    - "visual closed loop control for pouring liquids"
    - "what happens if learning to predict the effect of forces in images"
    - "newtonian image understanding unfolding the dynamics of objects in static images"
    - "recurrent instance segmentation"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-4 (induced by source paper "reasoning about liquids via closed loop simulation"):
  with members:
    - "visual closed loop control for pouring liquids"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "visual closed loop control for pouring liquids"):
  with members:
    - "lstm a search space odyssey"
    - "guided policy search with delayed senor measurements"
    - "multi sensor surface analysis for robotic ironing"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"
    - "learning to place new objects in a scene"

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.RO(Robotics)