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: "a domain based approach to social relation recognition"
  Abstract: Social relations are the foundation of human daily life. Developing techniques to analyze such relations from visual data bears great potential to build machines that better understand us and are capable of interacting with us at a social level. Previous investigations have remained partial due to the overwhelming diversity and complexity of the topic and consequently have only focused on a handful of social relations. In this paper, we argue that the domain-based theory from social psychology is a great starting point to systematically approach this problem. The theory provides coverage of all aspects of social relations and equally is concrete and predictive about the visual attributes and behaviors defining the relations included in each domain. We provide the first dataset built on this holistic conceptualization of social life that is composed of a hierarchical label space of social domains and social relations. We also contribute the first models to recognize such domains and relations and find superior performance for attribute based features. Beyond the encouraging performance of the attribute based approach, we also find interpretable features that are in accordance with the predictions from social psychology literature. Beyond our findings, we believe that our contributions more tightly interleave visual recognition and social psychology theory that has the potential to complement the theoretical work in the area with empirical and data-driven models of social life.

Connected through hyperedge HE-1 (induced by source paper "meta transfer learning through hard tasks"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "lgm net learning to generate matching networks for few shot learning"
    - "a closer look at few shot classification"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "variational context exploiting visual and textual context for grounding referring expressions"):
  with members:
    - "you only look listen once towards fast and accurate visual grounding"
    - "real time referring expression comprehension by single stage grounding network"
    - "mattnet modular attention network for referring expression comprehension"
    - "vip cnn visual phrase guided convolutional neural network"
    - "grounding referring expressions in images by variational context"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "comprehension guided referring expressions"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-3 (induced by source paper "learning to customize and combine deep learners for few shot learning"):
  with members:
    - "finding task relevant features for few shot learning by category traversal"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "learning embedding adaptation for few shot learning"
    - "meta learning update rules for unsupervised representation learning"
    - "how to train your maml"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "meta transfer learning for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "multi granularity reasoning for social relation recognition from images"):
  with members:
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "simple baselines for human pose estimation and tracking"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "dual glance model for deciphering social relationships"
    - "mask r cnn"
    - "semantic object parsing with graph lstm"
    - "from facial expression recognition to interpersonal relation prediction"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-5 (induced by source paper "an end to end network for generating social relationship graphs"):
  with members:
    - "person recognition in personal photo collections"
    - "neural motifs scene graph parsing with global context"
    - "dual glance model for deciphering social relationships"
    - "scene graph generation by iterative message passing"
    - "scene graph generation from objects phrases and region captions"
    - "gated graph sequence neural networks"
    - "spice semantic propositional image caption evaluation"
    - "graph structured representations for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "improving generalization via attribute selection on out of the box data"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "from zero shot learning to cold start recommendation"
    - "attribute guided network for cross modal zero shot hashing"
    - "complementary attributes a new clue to zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "transductive zero shot learning with adaptive structural embedding"
    - "label embedding for image classification"

Connected through hyperedge HE-7 (induced by source paper "social relation recognition in egocentric photostreams"):
  with members:
    - "person recognition in personal photo collections"
    - "batch based activity recognition from egocentric photo streams revisited"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "dockerface an easy to install and use faster r cnn face detector in a docker container"
    - "an overview of multi task learning in deep neural networks"
    - "with whom do i interact detecting social interactions in egocentric photo streams"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "meta transfer learning for few shot learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "meta learning with latent embedding optimization"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "taskonomy disentangling task transfer learning"
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "learning structure and strength of cnn filters for small sample size training"

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.CV(Computer Vision and Pattern Recognition)