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: "end to end differentiable proving"
  Abstract: We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specifically, we replace symbolic unification with a differentiable computation on vector representations of symbols using a radial basis function kernel, thereby combining symbolic reasoning with learning subsymbolic vector representations. By using gradient descent, the resulting neural network can be trained to infer facts from a given incomplete knowledge base. It learns to (i) place representations of similar symbols in close proximity in a vector space, (ii) make use of such similarities to prove queries, (iii) induce logical rules, and (iv) use provided and induced logical rules for multi-hop reasoning. We demonstrate that this architecture outperforms ComplEx, a state-of-the-art neural link prediction model, on three out of four benchmark knowledge bases while at the same time inducing interpretable function-free first-order logic rules.

Connected through hyperedge HE-1 (induced by source paper "knowledge graphs"):
  with members:
    - "modl a modular ontology design library"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "hypernetwork knowledge graph embeddings"
    - "ceres distantly supervised relation extraction from the semi structured web"
    - "a tutorial on modular ontology modeling with ontology design patterns the cooking recipes ontology"
    - "building an ecosystem for the tyrolean tourism knowledge graph"
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base"):
  with members:
    - "generating logical forms from graph representations of text and entities"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "neural query language a knowledge base query language for tensorflow"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "neural compositional denotational semantics for question answering"
    - "improving text to sql evaluation methodology"
    - "embedding logical queries on knowledge graphs"

Connected through hyperedge HE-3 (induced by source paper "deep adaptive semantic logic dasl compiling declarative knowledge into deep neural networks"):
  with members:
    - "augmenting neural networks with first order logic"
    - "neural theorem provers do not learn rules without exploration"
    - "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"
    - "semantically regularized logic graph embeddings"
    - "pytorch an imperative style high performance deep learning library"
    - "backpropagating through structured argmax using a spigot"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"

Connected through hyperedge HE-4 (induced by source paper "relational neural machines"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "neural logic machines"
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "neural logic reinforcement learning"
    - "neural markov logic networks"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"
    - "on a convex logic fragment for learning and reasoning"

Connected through hyperedge HE-5 (induced by source paper "from statistical relational to neuro symbolic artificial intelligence"):
  with members:
    - "relational neural machines"
    - "neural logic machines"
    - "differentiable reasoning on large knowledge bases and natural language"
    - "playgol learning programs through play"
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "synthesizing datalog programs using numerical relaxation"
    - "learning relational representations with auto encoding logic programs"

Connected through hyperedge HE-6 (induced by source paper "turning 30 new ideas in inductive logic programming"):
  with members:
    - "learning higher order logic programs"
    - "on the measure of intelligence"
    - "neural logic machines"
    - "logical reduction of metarules"
    - "forgetting to learn logic programs"
    - "playgol learning programs through play"
    - "synthesizing datalog programs using numerical relaxation"
    - "learning relational representations with auto encoding logic programs"

Connected through hyperedge HE-7 (induced by source paper "teaching the old dog new tricks supervised learning with constraints"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "deepproblog neural probabilistic logic programming"
    - "a convex framework for fair regression"
    - "equality of opportunity in supervised learning"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "a confidence based approach for balancing fairness and accuracy"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "efficient piecewise training of deep structured models for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "neural logic machines"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "deepproblog neural probabilistic logic programming"
    - "logical rule induction and theory learning using neural theorem proving"
    - "tunneling neural perception and logic reasoning through abductive learning"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "learning explanatory rules from noisy data"
    - "robustfill neural program learning under noisy i o"
    - "differentiable learning of logical rules for knowledge base 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.NE(Neural and Evolutionary Computing)