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: "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.

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"
    - "nlprolog reasoning with weak unification for question answering in natural language"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "differentiable reasoning on large knowledge bases and natural language"):
  with members:
    - "nlprolog reasoning with weak unification for question answering in natural language"
    - "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 "learn to explain efficiently via neural logic inductive learning"):
  with members:
    - "neural logic machines"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "explaining explanations in ai"
    - "tucker tensor factorization for knowledge graph completion"
    - "inductive logic programming via differentiable deep neural logic networks"
    - "iterative visual reasoning beyond convolutions"
    - "logical rule induction and theory learning using neural theorem proving"

Connected through hyperedge HE-4 (induced by source paper "drum end to end differentiable rule mining on knowledge graphs"):
  with members:
    - "variational graph recurrent neural networks"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "semi implicit graph variational auto encoders"
    - "tucker tensor factorization for knowledge graph completion"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "graph embedding techniques applications and performance a survey"
    - "learning explanatory rules from noisy data"

Connected through hyperedge HE-5 (induced by source paper "neural theorem provers do not learn rules without exploration"):
  with members:
    - "neural logic machines"
    - "a comprehensive survey on graph neural networks"
    - "deepproblog neural probabilistic logic programming"
    - "logical rule induction and theory learning using neural theorem proving"
    - "measuring abstract reasoning in neural networks"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "end to end differentiable proving"

Connected through hyperedge HE-6 (induced by source paper "rudas synthetic datasets for rule learning and evaluation tools"):
  with members:
    - "neural logic machines"
    - "differentiable reasoning on large knowledge bases and natural language"
    - "deepproblog neural probabilistic logic programming"
    - "logical rule induction and theory learning using neural theorem proving"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "rdf2rules learning rules from rdf knowledge bases by mining frequent predicate cycles"

Connected through hyperedge HE-7 (induced by source paper "program synthesis performance constrained by non linear spatial relations in synthetic visual reasoning test"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "gamepad a learning environment for theorem proving"
    - "25 years of cnns can we compare to human abstraction capabilities"
    - "how transferable are features in deep 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.AI(Artificial Intelligence)