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: "are elephants bigger than butterflies reasoning about sizes of objects"
  Abstract: Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate that this is mainly attributed to the lack of a comprehensive repository of size information. In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web. By maximizing the joint likelihood of textual and visual observations, our method learns reliable relative size estimates, with no explicit human supervision. We introduce the relative size dataset and show that our method outperforms competitive textual and visual baselines in reasoning about size comparisons.

Connected through hyperedge HE-1 (induced by source paper "olmpics on what language model pre training captures"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "visualizing and measuring the geometry of bert"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "still a pain in the neck evaluating text representations on lexical composition"
    - "do nlp models know numbers probing numeracy in embeddings"
    - "learning and evaluating general linguistic intelligence"
    - "diversify your datasets analyzing generalization via controlled variance in adversarial datasets"

Connected through hyperedge HE-2 (induced by source paper "is the red square big malevic modeling adjectives leveraging visual contexts"):
  with members:
    - "linguistic generalization and compositionality in modern artificial neural networks"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "how clever is the film model and how clever can it be"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "comparatives quantifiers proportions a multi task model for the learning of quantities from vision"
    - "systematic generalization what is required and can it be learned"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "pragmatically informative image captioning with character level reference"

Connected through hyperedge HE-3 (induced by source paper "can a gorilla ride a camel learning semantic plausibility from text"):
  with members:
    - "universal dependency parsing from scratch"
    - "solving hard coreference problems"
    - "sp 10k a large scale evaluation set for selectional preference acquisition"
    - "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"
    - "do neural language representations learn physical commonsense"
    - "probing neural network comprehension of natural language arguments"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "annotation artifacts in natural language inference data"

Connected through hyperedge HE-4 (induced by source paper "how large are lions inducing distributions over quantitative attributes"):
  with members:
    - "numeracy for language models evaluating and improving their ability to predict numbers"
    - "robust handling of polysemy via sparse representations"
    - "improving temporal relation extraction with a globally acquired statistical resource"
    - "verb physics relative physical knowledge of actions and objects"
    - "how much is 131 million dollars putting numbers in perspective with compositional descriptions"
    - "globally normalized transition based neural networks"
    - "numerically grounded language models for semantic error correction"

Connected through hyperedge HE-5 (induced by source paper "see the glass half full reasoning about liquid containers their volume and content"):
  with members:
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "visual closed loop control for pouring liquids"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "learning physical intuition of block towers by example"
    - "what happens if learning to predict the effect of forces in images"
    - "a multipath network for object detection"
    - "newtonian image understanding unfolding the dynamics of objects in static images"
    - "dense optical flow prediction from a static image"

Connected through hyperedge HE-6 (induced by source paper "verb physics relative physical knowledge of actions and objects"):
  with members:
    - "ordinal common sense inference"

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)