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: "physical problem solving joint planning with symbolic geometric and dynamic constraints"
  Abstract: In this paper, we present a new task that investigates how people interact with and make judgments about towers of blocks. In Experiment~1, participants in the lab solved a series of problems in which they had to re-configure three blocks from an initial to a final configuration. We recorded whether they used one hand or two hands to do so. In Experiment~2, we asked participants online to judge whether they think the person in the lab used one or two hands. The results revealed a close correspondence between participants' actions in the lab, and the mental simulations of participants online. To explain participants' actions and mental simulations, we develop a model that plans over a symbolic representation of the situation, executes the plan using a geometric solver, and checks the plan's feasibility by taking into account the physical constraints of the scene. Our model explains participants' actions and judgments to a high degree of quantitative accuracy.

Connected through hyperedge HE-1 (induced by source paper "a survey of knowledge representation in service robotics"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "the limits and potentials of deep learning for robotics"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "blitznet a real time deep network for scene understanding"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"

Connected through hyperedge HE-2 (induced by source paper "learning manipulation under physics constraints with visual perception"):
  with members:
    - "learning model based planning from scratch"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "metacontrol for adaptive imagination based optimization"
    - "see the glass half full reasoning about liquid containers their volume and content"
    - "imagination augmented agents for deep reinforcement learning"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "long term image boundary extrapolation"

Connected through hyperedge HE-3 (induced by source paper "the limits and potentials of deep learning for robotics"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "on calibration of modern neural networks"
    - "domain adaptation for visual applications a comprehensive survey"
    - "progressive neural networks"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "sparse representation based open set recognition"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-4 (induced by source paper "relational inductive bias for physical construction in humans and machines"):
  with members:
    - "neural message passing for quantum chemistry"
    - "metacontrol for adaptive imagination based optimization"
    - "learning combinatorial optimization algorithms over graphs"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "gated graph sequence neural networks"
    - "long term image boundary extrapolation"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "learning physical intuition of block towers by example"

Connected through hyperedge HE-5 (induced by source paper "acquiring target stacking skills by goal parameterized deep reinforcement learning"):
  with members:
    - "see the glass half full reasoning about liquid containers their volume and content"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "learning physical intuition of block towers by example"
    - "long term image boundary prediction"
    - "newtonian image understanding unfolding the dynamics of objects in static images"
    - "learning visual predictive models of physics for playing billiards"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-6 (induced by source paper "towards explainable inference about object motion using qualitative reasoning"):
  with members:
    - "qtc3d extending the qualitative trajectory calculus to three dimensions"

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)