human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning continuous semantic representations of symbolic expressions"):
  Abstract: Combining abstract, symbolic reasoning with continuous neural reasoning is a grand challenge of representation learning. As a step in this direction, we propose a new architecture, called neural equivalence networks, for the problem of learning continuous semantic representations of algebraic and logical expressions. These networks are trained to represent semantic equivalence, even of expressions that are syntactically very different. The challenge is that semantic representations must be computed in a syntax-directed manner, because semantics is compositional, but at the same time, small changes in syntax can lead to very large changes in semantics, which can be difficult for continuous neural architectures. We perform an exhaustive evaluation on the task of checking equivalence on a highly diverse class of symbolic algebraic and boolean expression types, showing that our model significantly outperforms existing architectures.

Includes members:
  Member "deep network guided proof search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "teaching temporal logics to neural networks")
  Member "deepmath deep sequence models for premise selection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an experimental study of formula embeddings for automated theorem proving in first order logic")
  Member "programming with a differentiable forth interpreter":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from statistical relational to neuro symbolic artificial intelligence")
  Member "weight normalization a simple reparameterization to accelerate training of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "a convolutional attention network for extreme summarization of source code":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "neural random access machines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards modular algorithm induction")
  Member "neural programmer inducing latent programs with gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "learning program embeddings to propagate feedback on student code":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "teaching temporal logics to neural networks")

Question: Please tell me which class the source paper 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)