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: "broadcasting convolutional network for visual relational reasoning"
  Abstract: In this paper, we propose the Broadcasting Convolutional Network (BCN) that extracts key object features from the global field of an entire input image and recognizes their relationship with local features. BCN is a simple network module that collects effective spatial features, embeds location information and broadcasts them to the entire feature maps. We further introduce the Multi-Relational Network (multiRN) that improves the existing Relation Network (RN) by utilizing the BCN module. In pixel-based relation reasoning problems, with the help of BCN, multiRN extends the concept of `pairwise relations' in conventional RNs to `multiwise relations' by relating each object with multiple objects at once. This yields in O(n) complexity for n objects, which is a vast computational gain from RNs that take O(n^2). Through experiments, multiRN has achieved a state-of-the-art performance on CLEVR dataset, which proves the usability of BCN on relation reasoning problems.

Connected through hyperedge HE-1 (induced by source paper "language conditioned graph networks for relational reasoning"):
  with members:
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "murel multimodal relational reasoning for visual question answering"
    - "graph neural networks a review of methods and applications"
    - "iterative visual reasoning beyond convolutions"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "classifying collisions with spatio temporal action graph networks"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"

Connected through hyperedge HE-2 (induced by source paper "urnet user resizable residual networks with conditional gating module"):
  with members:
    - "big little net an efficient multi scale feature representation for visual and speech recognition"
    - "efficient neural architecture search via parameter sharing"
    - "nisp pruning networks using neuron importance score propagation"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "adaptive neural networks for efficient inference"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "improving the robustness of capsule networks to image affine transformations"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "attention routing between capsules"
    - "generalized capsule networks with trainable routing procedure"
    - "neural network encapsulation"
    - "group equivariant capsule networks"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "sparse unsupervised capsules generalize better"
    - "capsgan using dynamic routing for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "sparse unsupervised capsules generalize better"):
  with members:
    - "dynamic routing between capsules"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "adversarial examples in the physical world"
    - "towards biologically plausible deep learning"
    - "winner take all autoencoders"
    - "k sparse autoencoders"
    - "building high level features using large scale unsupervised learning"

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)