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: "probabilistic reasoning via deep learning neural association models"
  Abstract: In this paper, we propose a new deep learning approach, called neural association model (NAM), for probabilistic reasoning in artificial intelligence. We propose to use neural networks to model association between any two events in a domain. Neural networks take one event as input and compute a conditional probability of the other event to model how likely these two events are to be associated. The actual meaning of the conditional probabilities varies between applications and depends on how the models are trained. In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks (DNN) and relation-modulated neural nets (RMNN), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multi-relational knowledge bases and commonsense reasoning. Experimental results on several popular datasets derived from WordNet, FreeBase and ConceptNet have all demonstrated that both DNNs and RMNNs perform equally well and they can significantly outperform the conventional methods available for these reasoning tasks. Moreover, compared with DNNs, RMNNs are superior in knowledge transfer, where a pre-trained model can be quickly extended to an unseen relation after observing only a few training samples. To further prove the effectiveness of the proposed models, in this work, we have applied NAMs to solving challenging Winograd Schema (WS) problems. Experiments conducted on a set of WS problems prove that the proposed models have the potential for commonsense reasoning.

Connected through hyperedge HE-1 (induced by source paper "joint embedding learning of educational knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "knowledge forest a novel model to organize knowledge fragments"
    - "incorporating literals into knowledge graph embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "character word lstm language models"

Connected through hyperedge HE-2 (induced by source paper "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "neural collective entity linking"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "learning over knowledge base embeddings for recommendation"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "adversarial personalized ranking for recommendation"
    - "explicit state tracking with semi supervisionfor neural dialogue generation"

Connected through hyperedge HE-3 (induced by source paper "unsupervised deep structured semantic models for commonsense reasoning"):
  with members:
    - "solving hard coreference problems"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "a simple method for commonsense reasoning"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"
    - "event2mind commonsense inference on events intents and reactions"
    - "record bridging the gap between human and machine commonsense reading comprehension"
    - "swag a large scale adversarial dataset for grounded commonsense inference"

Connected through hyperedge HE-4 (induced by source paper "attention is not all you need for commonsense reasoning"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "solving hard coreference problems"
    - "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"
    - "a simple method for commonsense reasoning"
    - "on the evaluation of common sense reasoning in natural language understanding"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "fact discovery from knowledge base via facet decomposition"):
  with members:
    - "analogical inference for multi relational embeddings"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "holographic embeddings of knowledge graphs"
    - "adam a method for stochastic optimization"
    - "question answering with subgraph embeddings"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "learning new facts from knowledge bases with neural tensor networks and semantic word vectors"

Connected through hyperedge HE-6 (induced by source paper "knowledge representation learning a quantitative review"):
  with members:
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "a neural knowledge language model"
    - "analogical inference for multi relational embeddings"
    - "visual translation embedding network for visual relation detection"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"

Connected through hyperedge HE-7 (induced by source paper "a simple method for commonsense reasoning"):
  with members:
    - "solving hard coreference problems"
    - "achieving human parity on automatic chinese to english news translation"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "fine tuned language models for text classification"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"
    - "broad context language modeling as reading comprehension"

Connected through hyperedge HE-8 (induced by source paper "linknbed multi graph representation learning with entity linkage"):
  with members:
    - "representation learning on graphs methods and applications"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "knowledge base completion baselines strike back"
    - "rdf graph alignment with bisimulation"
    - "generic statistical relational entity resolution in knowledge graphs"
    - "cross graph learning of multi relational associations"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "cross domain entity resolution in social media"

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)