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: "guiding high performance sat solvers with unsat core predictions"
  Abstract: The NeuroSAT neural network architecture was recently introduced for predicting properties of propositional formulae. When trained to predict the satisfiability of toy problems, it was shown to find solutions and unsatisfiable cores on its own. However, the authors saw "no obvious path" to using the architecture to improve the state-of-the-art. In this work, we train a simplified NeuroSAT architecture to directly predict the unsatisfiable cores of real problems. We modify several high-performance SAT solvers to periodically replace their variable activity scores with NeuroSAT's prediction of how likely the variables are to appear in an unsatisfiable core. The modified MiniSat solves 10% more problems on SAT-COMP 2018 within the standard 5,000 second timeout than the original does. The modified Glucose solves 11% more problems than the original, while the modified Z3 solves 6% more. The gains are even greater when the training is specialized for a specific distribution of problems; on a benchmark of hard problems from a scheduling domain, the modified Glucose solves 20% more problems than the original does within a one-hour timeout. Our results demonstrate that NeuroSAT can provide effective guidance to high-performance SAT solvers on real problems.

Connected through hyperedge HE-1 (induced by source paper "teaching temporal logics to neural networks"):
  with members:
    - "mathematical reasoning in latent space"
    - "enhancing the transformer with explicit relational encoding for math problem solving"
    - "analysing mathematical reasoning abilities of neural models"
    - "deep learning for symbolic mathematics"
    - "graph representations for higher order logic and theorem proving"
    - "holist an environment for machine learning of higher order theorem proving"
    - "a comprehensive survey on graph neural networks"
    - "structured neural summarization"

Connected through hyperedge HE-2 (induced by source paper "it s not what machines can learn it s what we cannot teach"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "generating difficult sat instances by preventing triangles"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"
    - "a survey on bias and fairness in machine learning"
    - "deep learning for symbolic mathematics"
    - "the use of deep learning for symbolic integration a review of lample and charton 2019"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-3 (induced by source paper "nlocalsat boosting local search with solution prediction"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "graph based convolutional neural network"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (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"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "smart predict and optimize for hard combinatorial optimization problems"
    - "end to end learning and optimization on graphs"
    - "learning a sat solver from single bit supervision"
    - "direct optimization through arg max for discrete variational auto encoder"

Connected through hyperedge HE-5 (induced by source paper "graph representations for higher order logic and theorem proving"):
  with members:
    - "neural logic machines"
    - "generating correctness proofs with neural networks"
    - "a comprehensive survey on graph neural networks"
    - "gamepad a learning environment for theorem proving"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "relational inductive biases deep learning and graph networks"
    - "can neural networks understand logical entailment"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "improving sat solver heuristics with graph networks and reinforcement learning"):
  with members:
    - "reinforcement learning driven heuristic optimization"
    - "learning transferable cooperative behavior in multi agent teams"
    - "graph representations for higher order logic and theorem proving"
    - "fast graph representation learning with pytorch geometric"
    - "structured agents for physical construction"
    - "deep multi agent reinforcement learning with relevance graphs"
    - "learning a sat solver from single bit supervision"
    - "graph networks as learnable physics engines for inference and control"

Connected through hyperedge HE-7 (induced by source paper "cln2inv learning loop invariants with continuous logic networks"):
  with members:
    - "inductive logic programming via differentiable deep neural logic networks"
    - "learning a sat solver from single bit supervision"
    - "learning explanatory rules from noisy data"
    - "inferring loop invariants by mutation dynamic analysis and static checking"
    - "adam a method for stochastic optimization"
    - "loop invariants analysis classification and examples"

Connected through hyperedge HE-8 (induced by source paper "a formal proof of pac learnability for decision stumps"):
  with members:
    - "gamepad a learning environment for theorem proving"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "developing bug free machine learning systems with formal mathematics"
    - "on the expressive power of deep learning a tensor analysis"
    - "the foundational cryptography framework"

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)