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: "deep learning for symbolic mathematics"
  Abstract: Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations. We propose a syntax for representing mathematical problems, and methods for generating large datasets that can be used to train sequence-to-sequence models. We achieve results that outperform commercial Computer Algebra Systems such as Matlab or Mathematica.

Connected through hyperedge HE-1 (induced by source paper "natural language qa approaches using reasoning with external knowledge"):
  with members:
    - "transformers as soft reasoners over language"
    - "recent advances in natural language inference a survey of benchmarks resources and approaches"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "piqa reasoning about physical commonsense in natural language"
    - "language models as knowledge bases"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "understanding roles and entities datasets and models for natural language inference"
    - "qasc a dataset for question answering via sentence composition"

Connected through hyperedge HE-2 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"

Connected through hyperedge HE-3 (induced by source paper "teaching temporal logics to neural networks"):
  with members:
    - "mathematical reasoning in latent space"
    - "guiding high performance sat solvers with unsat core predictions"
    - "enhancing the transformer with explicit relational encoding for math problem solving"
    - "analysing mathematical reasoning abilities of neural models"
    - "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-4 (induced by source paper "graph neural networks meet neural symbolic computing a survey and perspective"):
  with members:
    - "the next decade in ai four steps towards robust artificial intelligence"
    - "run csp unsupervised learning of message passing networks for binary constraint satisfaction problems"
    - "recurrent independent mechanisms"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "the use of deep learning for symbolic integration a review of lample and charton 2019"
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-5 (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"
    - "guiding high performance sat solvers with unsat core predictions"
    - "a survey on bias and fairness in machine learning"
    - "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-6 (induced by source paper "computational model discovery with reinforcement learning"):
  with members:
    - "coarse scale pdes from fine scale observations via machine learning"
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "data driven discovery of free form governing differential equations"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "learning dexterous in hand manipulation"
    - "neural ordinary differential equations"
    - "automatic derivation of formulas using reforcement learning"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"

Connected through hyperedge HE-7 (induced by source paper "a neural network model of perception and reasoning"):
  with members:
    - "deep visual domain adaptation a survey"
    - "deep learning a critical appraisal"
    - "opening the black box of deep neural networks via information"
    - "learning both weights and connections for efficient neural networks"
    - "intriguing properties of 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.SC(Symbolic Computation)