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: "a dataset of daily interactive manipulation"
  Abstract: Robots that succeed in factories stumble to complete the simplest daily task humans take for granted, for the change of environment makes the task exceedingly difficult. Aiming to teach robot perform daily interactive manipulation in a changing environment using human demonstrations, we collected our own data of interactive manipulation. The dataset focuses on position, orientation, force, and torque of objects manipulated in daily tasks. The dataset includes 1,593 trials of 32 types of daily motions and 1,596 trials of pouring alone, as well as helper code. We present our dataset to facilitate the research on task-oriented interactive manipulation.

Connected through hyperedge HE-1 (induced by source paper "robust robotic pouring using audition and haptics"):
  with members:
    - "making sense of audio vibration for liquid height estimation in robotic pouring"
    - "my lips are concealed audio visual speech enhancement through obstructions"
    - "accurate pouring with an autonomous robot using an rgb d camera"
    - "liquid pouring monitoring via rich sensory inputs"
    - "learning to pour"
    - "perceiving and reasoning about liquids using fully convolutional networks"
    - "see the glass half full reasoning about liquid containers their volume and content"
    - "reasoning about liquids via closed loop simulation"

Connected through hyperedge HE-2 (induced by source paper "tuned inception v3 for recognizing states of cooking ingredients"):
  with members:
    - "cooking state recognition from images using inception architecture"
    - "long activity video understanding using functional object oriented network"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "images and recipes retrieval in the cooking context"
    - "classifying cooking object s state using a tuned vgg convolutional neural network"
    - "functional object oriented network for manipulation learning"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-3 (induced by source paper "accurate robotic pouring for serving drinks"):
  with members:
    - "accurate pouring with an autonomous robot using an rgb d camera"
    - "functional object oriented network construction expansion"
    - "learning to pour"
    - "functional object oriented network for manipulation learning"
    - "visual closed loop control for pouring liquids"
    - "synthesis of recurrent neural networks for dynamical system simulation"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "manipulation motion taxonomy and coding for robots"):
  with members:
    - "a survey of knowledge representation in service robotics"
    - "long activity video understanding using functional object oriented network"
    - "scaling egocentric vision the epic kitchens dataset"
    - "functional object oriented network construction expansion"
    - "functional object oriented network for manipulation learning"

Connected through hyperedge HE-5 (induced by source paper "benchmarking simulated robotic manipulation through a real world dataset"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "quantifying the reality gap in robotic manipulation tasks"
    - "learning dexterous in hand manipulation"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"

Connected through hyperedge HE-6 (induced by source paper "estimating forces of robotic pouring using a lstm rnn"):
  with members:
    - "lstm a search space odyssey"
    - "gate variants of gated recurrent unit gru neural networks"
    - "functional object oriented network for manipulation learning"

Connected through hyperedge HE-7 (induced by source paper "characterizing input methods for human to robot demonstrations"):
  with members:
    - "inferring geometric constraints in human demonstrations"

Connected through hyperedge HE-8 (induced by source paper "ai meets physical world exploring robot cooking"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "learning to pour"
    - "functional object oriented network for manipulation learning"
    - "datasets on object manipulation and interaction a survey"
    - "generating sequences with recurrent neural networks"

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)