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: "end to end learning and optimization on graphs"
  Abstract: Real-world applications often combine learning and optimization problems on graphs. For instance, our objective may be to cluster the graph in order to detect meaningful communities (or solve other common graph optimization problems such as facility location, maxcut, and so on). However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization. Standard approaches treat learning and optimization entirely separately, while recent machine learning work aims to predict the optimal solution directly from the inputs. Here, we propose an alternative decision-focused learning approach that integrates a differentiable proxy for common graph optimization problems as a layer in learned systems. The main idea is to learn a representation that maps the original optimization problem onto a simpler proxy problem that can be efficiently differentiated through. Experimental results show that our ClusterNet system outperforms both pure end-to-end approaches (that directly predict the optimal solution) and standard approaches that entirely separate learning and optimization. Code for our system is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "decision trees for decision making under the predict then optimize framework"):
  with members:
    - "learning optimal and fair decision trees for non discriminative decision making"
    - "smart predict and optimize for hard combinatorial optimization problems"
    - "optimal sparse decision trees"
    - "generalization bounds in the predict then optimize framework"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "task based end to end model learning in stochastic optimization"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "optimal generalized decision trees via integer programming"

Connected through hyperedge HE-2 (induced by source paper "differentiation of blackbox combinatorial solvers"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "guiding high performance sat solvers with unsat core predictions"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "smart predict and optimize for hard combinatorial optimization problems"
    - "learning a sat solver from single bit supervision"
    - "direct optimization through arg max for discrete variational auto encoder"

Connected through hyperedge HE-3 (induced by source paper "task based learning via task oriented prediction network"):
  with members:
    - "decision focused learning of adversary behavior in security games"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "task based end to end model learning in stochastic optimization"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "adam a method for stochastic optimization"

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.LG(Machine Learning)