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: "row less universal schema"
  Abstract: Universal schema jointly embeds knowledge bases and textual patterns to reason about entities and relations for automatic knowledge base construction and information extraction. In the past, entity pairs and relations were represented as learned vectors with compatibility determined by a scoring function, limiting generalization to unseen text patterns and entities. Recently, 'column-less' versions of Universal Schema have used compositional pattern encoders to generalize to all text patterns. In this work we take the next step and propose a 'row-less' model of universal schema, removing explicit entity pair representations. Instead of learning vector representations for each entity pair in our training set, we treat an entity pair as a function of its relation types. In experimental results on the FB15k-237 benchmark we demonstrate that we can match the performance of a comparable model with explicit entity pair representations using a model of attention over relation types. We further demonstrate that the model per- forms with nearly the same accuracy on entity pairs never seen during training.

Connected through hyperedge HE-1 (induced by source paper "rewarding coreference resolvers for being consistent with world knowledge"):
  with members:
    - "incorporating context and external knowledge for pronoun coreference resolution"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "end to end neural coreference resolution"
    - "distral robust multitask reinforcement learning"
    - "deep reinforcement learning for mention ranking coreference models"
    - "multilingual relation extraction using compositional universal schema"

Connected through hyperedge HE-2 (induced by source paper "matching the blanks distributional similarity for relation learning"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "position aware self attention with relative positional encodings for slot filling"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "holographic embeddings of knowledge graphs"
    - "relation classification via recurrent neural network"

Connected through hyperedge HE-3 (induced by source paper "termite a system for tunneling through heterogeneous data"):
  with members:
    - "embedding text in hyperbolic spaces"
    - "holographic embeddings of knowledge graphs"
    - "deep metric learning using triplet network"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "a neural knowledge language model"):
  with members:
    - "pointing the unknown words"
    - "leveraging lexical resources for learning entity embeddings in multi relational data"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "dialog based language learning"
    - "neural text generation from structured data with application to the biography domain"
    - "exploring the limits of language modeling"
    - "pointer sentinel mixture models"
    - "neural generative question answering"

Connected through hyperedge HE-5 (induced by source paper "attending to all mention pairs for full abstract biological relation extraction"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "attention is all you need"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "noise mitigation for neural entity typing and relation extraction"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-6 (induced by source paper "lifted rule injection for relation embeddings"):
  with members:
    - "harnessing deep neural networks with logic rules"
    - "multilingual relation extraction using compositional universal schema"
    - "compositional vector space models for knowledge base completion"
    - "adam a method for stochastic optimization"
    - "retrofitting word vectors to semantic lexicons"
    - "recursive neural networks can learn logical semantics"
    - "bpr bayesian personalized ranking from implicit feedback"

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)