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: "nlprolog reasoning with weak unification for question answering in natural language"
  Abstract: Rule-based models are attractive for various tasks because they inherently lead to interpretable and explainable decisions and can easily incorporate prior knowledge. However, such systems are difficult to apply to problems involving natural language, due to its linguistic variability. In contrast, neural models can cope very well with ambiguity by learning distributed representations of words and their composition from data, but lead to models that are difficult to interpret. In this paper, we describe a model combining neural networks with logic programming in a novel manner for solving multi-hop reasoning tasks over natural language. Specifically, we propose to use a Prolog prover which we extend to utilize a similarity function over pretrained sentence encoders. We fine-tune the representations for the similarity function via backpropagation. This leads to a system that can apply rule-based reasoning to natural language, and induce domain-specific rules from training data. We evaluate the proposed system on two different question answering tasks, showing that it outperforms two baselines -- BIDAF (Seo et al., 2016a) and FAST QA (Weissenborn et al., 2017b) on a subset of the WikiHop corpus and achieves competitive results on the MedHop data set (Welbl et al., 2017).

Connected through hyperedge HE-1 (induced by source paper "transformers as soft reasoners over language"):
  with members:
    - "differentiable reasoning on large knowledge bases and natural language"
    - "learning deep transformer models for machine translation"
    - "probing natural language inference models through semantic fragments"
    - "improving graph neural network representations of logical formulae with subgraph pooling"
    - "analysing mathematical reasoning abilities of neural models"
    - "establishing strong baselines for the new decade sequence tagging syntactic and semantic parsing with bert"
    - "quartz an open domain dataset of qualitative relationship questions"
    - "clutrr a diagnostic benchmark for inductive reasoning from text"

Connected through hyperedge HE-2 (induced by source paper "differentiable reasoning on large knowledge bases and natural language"):
  with members:
    - "towards neural theorem proving at scale"
    - "a survey on open information extraction"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "billion scale similarity search with gpus"
    - "end to end differentiable proving"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "neural probabilistic logic programming in deepproblog"):
  with members:
    - "neural logic machines"
    - "playgol learning programs through play"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"
    - "neural guided constraint logic programming for program synthesis"
    - "learning explanatory rules from noisy data"
    - "a simple neural network module for relational reasoning"

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.CL(Computation and Language)