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 "towards neural theorem proving at scale"):
  Abstract: Neural models combining representation learning and reasoning in an end-to-end trainable manner are receiving increasing interest. However, their use is severely limited by their computational complexity, which renders them unusable on real world datasets. We focus on the Neural Theorem Prover (NTP) model proposed by Rockt{\"{a}}schel and Riedel (2017), a continuous relaxation of the Prolog backward chaining algorithm where unification between terms is replaced by the similarity between their embedding representations. For answering a given query, this model needs to consider all possible proof paths, and then aggregate results - this quickly becomes infeasible even for small Knowledge Bases (KBs). We observe that we can accurately approximate the inference process in this model by considering only proof paths associated with the highest proof scores. This enables inference and learning on previously impracticable KBs.

Includes members:
  Member "deep learning a critical appraisal":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "learning explanatory rules from noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "end to end differentiable proving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "knowledge graphs")
  Member "scaling memory augmented neural networks with sparse reads and writes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "memory based graph networks")
  Member "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "neuro symbolic program synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "terpret a probabilistic programming language for program induction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "why build an assistant in minecraft")
  Member "practical and optimal lsh for angular distance":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "differentiable reasoning over a virtual knowledge base")

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.AI(Artificial Intelligence)