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: "generalizing to unseen entities and entity pairs with row less universal schema"
  Abstract: Universal schema predicts the types of entities and relations in a knowledge base (KB) by jointly embedding the union of all available schema types---not only types from multiple structured databases (such as Freebase or Wikipedia infoboxes), but also types expressed as textual patterns from raw text. This prediction is typically modeled as a matrix completion problem, with one type per column, and either one or two entities per row (in the case of entity types or binary relation types, respectively). Factorizing this sparsely observed matrix yields a learned vector embedding for each row and each column. In this paper we explore the problem of making predictions for entities or entity-pairs unseen at training time (and hence without a pre-learned row embedding). We propose an approach having no per-row parameters at all; rather we produce a row vector on the fly using a learned aggregation function of the vectors of the observed columns for that row. We experiment with various aggregation functions, including neural network attention models. Our approach can be understood as a natural language database, in that questions about KB entities are answered by attending to textual or database evidence. In experiments predicting both relations and entity types, we demonstrate that despite having an order of magnitude fewer parameters than traditional universal schema, we can match the accuracy of the traditional model, and more importantly, we can now make predictions about unseen rows with nearly the same accuracy as rows available at training time.

Connected through hyperedge HE-1 (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-2 (induced by source paper "simultaneously linking entities and extracting relations from biomedical text without mention level supervision"):
  with members:
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "improving entity linking by modeling latent relations between mentions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "deep joint entity disambiguation with local neural attention"

Connected through hyperedge HE-3 (induced by source paper "representation learning with multisets"):
  with members:
    - "hyperbolic disk embeddings for directed acyclic graphs"
    - "rep the set neural networks for learning set representations"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "attention based deep multiple instance learning"
    - "deep sets"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "order embeddings of images and language"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "relation discovery with out of relation knowledge base as supervision"):
  with members:
    - "variational knowledge graph reasoning"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "incorporating relation paths in neural relation extraction"
    - "world knowledge as indirect supervision for document clustering"
    - "complex embeddings for simple link prediction"
    - "multilingual relation extraction using compositional universal schema"

Connected through hyperedge HE-5 (induced by source paper "openki integrating open information extraction and knowledge bases with relation inference"):
  with members:
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "multilingual relation extraction using compositional universal schema"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "neural latent relational analysis to capture lexical semantic relations in a vector space"):
  with members:
    - "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"
    - "analogical inference for multi relational embeddings"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "complex embeddings for simple link prediction"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "holographic embeddings of knowledge graphs"
    - "embedding semantic relations into word representations"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-7 (induced by source paper "neural compositional denotational semantics for question answering"):
  with members:
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "learning visual reasoning without strong priors"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "learning a natural language interface with neural programmer"
    - "learning to compose neural networks for question answering"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-8 (induced by source paper "hierarchical losses and new resources for fine grained entity typing and linking"):
  with members:
    - "attention is all you need"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "poincar e embeddings for learning hierarchical representations"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "noise mitigation for neural entity typing and relation extraction"
    - "complex embeddings for simple link prediction"
    - "classifying relations by ranking with convolutional 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.CL(Computation and Language)