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: "hamiltonian neural networks"
  Abstract: Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? In this paper, we draw inspiration from Hamiltonian mechanics to train models that learn and respect exact conservation laws in an unsupervised manner. We evaluate our models on problems where conservation of energy is important, including the two-body problem and pixel observations of a pendulum. Our model trains faster and generalizes better than a regular neural network. An interesting side effect is that our model is perfectly reversible in time.

Connected through hyperedge HE-1 (induced by source paper "automatic differentiation and continuous sensitivity analysis of rigid body dynamics"):
  with members:
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "deepmind control suite"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "flexible neural representation for physics prediction"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-2 (induced by source paper "differential dynamic programming neural optimizer"):
  with members:
    - "symplectic ode net learning hamiltonian dynamics with control"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "feedback network for image super resolution"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "deep learning theory review an optimal control and dynamical systems perspective"
    - "negative eigenvalues of the hessian in deep neural networks"
    - "deep predictive coding network for object recognition"

Connected through hyperedge HE-3 (induced by source paper "learning group structure and disentangled representations of dynamical environments"):
  with members:
    - "hamiltonian generative networks"
    - "symmetry based disentangled representation learning requires interaction with environments"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "decoupling feature extraction from policy learning assessing benefits of state representation learning in goal based robotics"
    - "a style based generator architecture for generative adversarial networks"
    - "neural ordinary differential equations"
    - "towards a definition of disentangled representations"
    - "a survey of inductive biases for factorial representation learning"

Connected through hyperedge HE-4 (induced by source paper "dissipative symoden encoding hamiltonian dynamics with dissipation and control into deep learning"):
  with members:
    - "symplectic ode net learning hamiltonian dynamics with control"
    - "a general framework for structured learning of mechanical systems"
    - "learning dynamical systems from partial observations"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "continuous control with deep reinforcement learning"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "lagrangian neural networks"):
  with members:
    - "hamiltonian graph networks with ode integrators"
    - "symplectic recurrent neural networks"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "phyre a new benchmark for physical reasoning"
    - "interaction networks for learning about objects relations and physics"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "encoding physical constraints in differentiable newton euler algorithm"):
  with members:
    - "a general framework for structured learning of mechanical systems"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "linear matrix inequalities for physically consistent inertial parameter identification a statistical perspective on the mass distribution"
    - "a new data source for inverse dynamics learning"
    - "identification of fully physical consistent inertial parameters using optimization on manifolds"

Connected through hyperedge HE-7 (induced by source paper "port hamiltonian approach to neural network training"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "large scale gan training for high fidelity natural image synthesis"
    - "deep neural networks motivated by partial differential equations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural ordinary differential equations"
    - "visualizing the loss landscape of neural nets"
    - "deep relaxation partial differential equations for optimizing deep neural networks"
    - "the expressive power of neural networks a view from the width"

Connected through hyperedge HE-8 (induced by source paper "symplectic ode net learning hamiltonian dynamics with control"):
  with members:
    - "a general framework for structured learning of mechanical systems"
    - "learning dynamical systems from partial observations"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "graph networks as learnable physics engines for inference and control"
    - "neural ordinary differential equations"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "continuous control with deep reinforcement learning"
    - "deep residual learning for image recognition"

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.NE(Neural and Evolutionary Computing)