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: "adversarial sets for regularising neural link predictors"
  Abstract: In adversarial training, a set of models learn together by pursuing competing goals, usually defined on single data instances. However, in relational learning and other non-i.i.d domains, goals can also be defined over sets of instances. For example, a link predictor for the is-a relation needs to be consistent with the transitivity property: if is-a(x_1, x_2) and is-a(x_2, x_3) hold, is-a(x_1, x_3) needs to hold as well. Here we use such assumptions for deriving an inconsistency loss, measuring the degree to which the model violates the assumptions on an adversarially-generated set of examples. The training objective is defined as a minimax problem, where an adversary finds the most offending adversarial examples by maximising the inconsistency loss, and the model is trained by jointly minimising a supervised loss and the inconsistency loss on the adversarial examples. This yields the first method that can use function-free Horn clauses (as in Datalog) to regularise any neural link predictor, with complexity independent of the domain size. We show that for several link prediction models, the optimisation problem faced by the adversary has efficient closed-form solutions. Experiments on link prediction benchmarks indicate that given suitable prior knowledge, our method can significantly improve neural link predictors on all relevant metrics.

Connected through hyperedge HE-1 (induced by source paper "analyzing differentiable fuzzy logic operators"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "semi supervised learning using differentiable reasoning"
    - "injecting prior knowledge for transfer learning into reinforcement learning algorithms using logic tensor networks"
    - "learning and t norms theory"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "deepproblog neural probabilistic logic programming"
    - "large scale gan training for high fidelity natural image synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (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-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "investigating robustness and interpretability of link prediction via adversarial modifications"):
  with members:
    - "understanding the origins of bias in word embeddings"
    - "adversarial attack on graph structured data"
    - "graph embedding techniques applications and performance a survey"
    - "embedding multimodal relational data for knowledge base completion"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "answering visual relational queries in web extracted knowledge graphs"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "towards interpretable deep neural networks by leveraging adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "logicenn a neural based knowledge graphs embedding model with logical rules"):
  with members:
    - "quaternion knowledge graph embedding"
    - "improving knowledge graph embedding using simple constraints"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "on multi relational link prediction with bilinear models"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "lifted rule injection for relation embeddings"

Connected through hyperedge HE-6 (induced by source paper "knowledge graph fact prediction via knowledge enriched tensor factorization"):
  with members:
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "knowledge base completion baselines strike back"
    - "convolutional 2d knowledge graph embeddings"
    - "lifted rule injection for relation embeddings"
    - "complex embeddings for simple link prediction"
    - "type constrained representation learning in knowledge graphs"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "embedding cardinality constraints in neural link predictors"):
  with members:
    - "improving knowledge graph embedding using simple constraints"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "cardinal virtues extracting relation cardinalities from text"
    - "predicting completeness in knowledge bases"
    - "poincar e embeddings for learning hierarchical representations"
    - "complex embeddings for simple link prediction"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "lyrics a general interface layer to integrate logic inference and deep learning"):
  with members:
    - "harnessing deep neural networks with logic rules"
    - "lifted rule injection for relation embeddings"
    - "tensorlog a differentiable deductive database"
    - "tensorflow a system for large scale machine learning"
    - "bidirectional lstm crf models for sequence tagging"
    - "hinge loss markov random fields and probabilistic soft logic"
    - "natural language processing almost from scratch"

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)